WO2023093582A1 - 雷达数据的压缩方法和装置 - Google Patents

雷达数据的压缩方法和装置 Download PDF

Info

Publication number
WO2023093582A1
WO2023093582A1 PCT/CN2022/132107 CN2022132107W WO2023093582A1 WO 2023093582 A1 WO2023093582 A1 WO 2023093582A1 CN 2022132107 W CN2022132107 W CN 2022132107W WO 2023093582 A1 WO2023093582 A1 WO 2023093582A1
Authority
WO
WIPO (PCT)
Prior art keywords
measurement data
code stream
distance measurement
signal strength
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2022/132107
Other languages
English (en)
French (fr)
Inventor
涂晨曦
蔡康颖
曹潇然
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Technologies Co Ltd
Original Assignee
Huawei Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Priority to EP22897665.0A priority Critical patent/EP4407959A4/en
Publication of WO2023093582A1 publication Critical patent/WO2023093582A1/zh
Priority to US18/672,686 priority patent/US20240314350A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/513—Processing of motion vectors
    • H04N19/517—Processing of motion vectors by encoding
    • H04N19/52—Processing of motion vectors by encoding by predictive encoding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4808—Evaluating distance, position or velocity data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/004—Predictors, e.g. intraframe, interframe coding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/005—Statistical coding, e.g. Huffman, run length coding
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/46—Conversion to or from run-length codes, i.e. by representing the number of consecutive digits, or groups of digits, of the same kind by a code word and a digit indicative of that kind
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/70—Type of the data to be coded, other than image and sound
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/16—Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
    • H04L69/164—Adaptation or special uses of UDP protocol
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/93—Run-length coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • the present application relates to radar technology, in particular to a method and device for compressing radar data.
  • LiDAR has now become a standard sensor for Level 4 autonomous driving and has been adopted by many mass-produced models.
  • lidar has gradually become a major pain point.
  • the number of lidar lines continues to increase, and the transmission of raw data of a single lidar may require a bandwidth of more than 100Mbps.
  • self-driving vehicles are usually equipped with various sensors such as cameras and millimeter-wave radars, which put more pressure on data transmission and storage.
  • the present application provides a radar data compression method and device, so that the amount of data to be compressed is greatly reduced, especially the more 0 values, the higher the compression efficiency, thereby saving the occupied code stream.
  • the present application provides a method for compressing radar data, including: obtaining a code stream; obtaining a first code stream corresponding to distance measurement data according to the code stream; decompressing the first code stream to obtain The distance measurement data; acquiring signal strength measurement data according to the distance measurement data.
  • the raw data of lidar refers to user datagram protocol (UDP) data packets or packet data. Arrange the UDP data to get grouped data. In the grouped data, there is still a data block (block) corresponding to a laser radar launch.
  • a block includes an azimuth angle measurement data and multiple sets of distance measurement data and signals. There is a one-to-one correspondence between the intensity measurement data, the distance measurement data and the signal strength measurement data.
  • the compression methods that can be used by the encoding end include:
  • run-length encoding is performed on the multiple zero-value distance measurement data to obtain the first code stream.
  • the data to be compressed includes multiple non-zero distance measurement data
  • residual-based entropy coding is performed on the multiple non-zero distance measurement data to obtain the first code stream.
  • the distance measurement data and the signal strength measurement data in the same group are also correlated.
  • the corresponding signal strength measurement data is also 0. This is because when the distance measurement data is 0, it means that the signal is too weak to obtain valid information, so ignore the distance measurement data and mark it as 0, and the corresponding The signal strength measurement data of is also recorded as 0. Therefore, cross-modal compression of the two can be considered to achieve optimal compression efficiency.
  • the encoding end does not need to process it, which can be greatly improved. Reduce the amount of data, the more 0 values, the higher the compression efficiency, thus saving the code stream occupied.
  • the values of distance measurement data and signal strength measurement data in the same group in addition to the above-mentioned relationship of 0 value to 0 value, may also have a calculation relationship between the two, which can greatly reduce Small amount of data, the more signal strength measurement data that has a calculation relationship with the distance measurement data, the higher the compression efficiency, thus saving the occupied code stream.
  • the distance measurement data when the distance measurement data is non-zero, predict the signal strength measurement data corresponding to the distance measurement data according to one or more non-zero distance measurement data to obtain the residual signal strength measurement data difference, and then obtain the second code stream according to the residual of the signal strength measurement data.
  • the data to be compressed includes a plurality of non-zero distance measurement data
  • residual-based entropy coding is performed on the plurality of signal strength measurement data corresponding to the plurality of non-zero distance measurement data to Get the second code stream.
  • the non-zero distance measurement data is acquired, and the non-zero signal strength measurement data corresponding to the non-zero distance measurement data is predicted according to one or more non-zero distance measurement data to obtain the first
  • the residual error is to predict another non-zero signal strength measurement data according to the non-zero signal strength measurement data to obtain a second residual error, and finally obtain a second code stream according to the first residual error and the second residual error.
  • the two are compressed in a cross-modal manner, so that the amount of data to be compressed is greatly reduced, especially 0
  • the method further includes: acquiring a third code stream corresponding to the time measurement data according to the code stream; and decompressing the third code stream to obtain the time measurement data.
  • the method further includes: acquiring a fourth code stream corresponding to the rotation angle measurement data according to the code stream; decompressing the fourth code stream to obtain the rotation angle measurement data data.
  • the above decompression includes run-length decoding, entropy decoding or residual-based entropy decoding.
  • the present application provides a method for compressing radar data, including: obtaining data to be compressed, the data to be compressed is the measurement data collected by laser radar N times, N ⁇ 1; when the data to be compressed When the corresponding distance measurement data and signal strength measurement data are included, compress the distance measurement data to obtain a first code stream; predict the signal strength measurement data according to the distance measurement data to obtain a second code stream .
  • the code stream includes at least one of the first code stream, the second code stream, the third code stream and the fourth code stream.
  • it is a solution in the city, mainly considering distance measurement data and signal strength measurement data, so in the code stream Need to include at least the first code stream.
  • some syntax elements may be carried in the code stream to indicate the meanings of different fields in the code stream.
  • the encoding end and the decoding end may agree on the lengths of different code streams in advance, and determine which code stream among the preceding four code streams according to the bytes or bits of the currently parsed code stream.
  • run-length decoding is performed on the first code stream to obtain a plurality of continuous zero-valued distance measurement data.
  • the decoding end can use run-length decoding for the corresponding fields in the code stream according to the instruction of the syntax elements, so as to obtain multiple consecutive 0-value distance measurement data.
  • entropy decoding is performed on the first code stream to obtain distance measurement data except a plurality of consecutive zero-value distance measurement data.
  • the signal strength measurement data corresponding to the distance measurement data is set to 0.
  • the signal strength measurement data corresponding to the distance measurement data is calculated only according to the distance measurement data; or, when the distance measurement data is non-zero, only according to the already
  • the one or more non-zero distance measurement data obtained by decoding are calculated to obtain signal strength measurement data corresponding to the distance measurement data.
  • the second code stream corresponding to the signal strength measurement data is obtained according to the code stream, and the second code stream is decompressed to obtain a non-zero residual of the signal strength measurement data.
  • One or more non-zero distance measurements and residuals result in non-zero signal strength measurements.
  • a second code stream corresponding to the signal strength measurement data is acquired according to the code stream, and the second code stream is decompressed to obtain non-zero signal strength measurement data.
  • the non-zero signal strength measurement data is predicted according to the decoded one or more non-zero distance measurement data, and another non-zero signal is predicted according to the non-zero signal strength measurement data Strength measurement data.
  • the two are decompressed in a cross-modal manner, because part of the signal strength measurement data in the code stream can save Its bit stream greatly reduces the code stream length and improves compression efficiency.
  • the method further includes: when the data to be compressed further includes time measurement data, performing run-length coding, entropy coding, or residual-based entropy coding on the time measurement data to obtain the first Three code streams; and/or, when the data to be compressed further includes rotation angle measurement data, perform run-length coding, entropy coding or residual-based entropy coding on the rotation angle measurement data to obtain a fourth code stream.
  • the present application provides a method for compressing radar data, including: obtaining data to be compressed, the data to be compressed is the measurement data collected by laser radar N times, N ⁇ 1; when the data to be compressed When multiple zero-value distance measurement data are included, run-length coding is performed on the multiple zero-value distance measurement data to obtain a first code stream.
  • the method further includes: when the data to be compressed includes multiple non-zero distance measurement data, performing residual-based entropy coding on the multiple non-zero distance measurement data to obtain the first code stream.
  • the method further includes: when the data to be compressed further includes signal strength measurement data, performing run-length coding, entropy coding, or residual-based entropy coding on the signal strength measurement data to obtain a second code stream; and/or, when the data to be compressed further includes time measurement data, perform run-length coding, entropy coding, or residual-based entropy coding on the time measurement data to obtain a third code stream; And/or, when the data to be compressed further includes rotation angle measurement data, run-length coding, entropy coding, or residual-based entropy coding is performed on the rotation angle measurement data to obtain a fourth code stream.
  • the present application provides a method for decompressing radar data, including: obtaining a code stream; obtaining a first code stream corresponding to distance measurement data according to the code stream; performing run-length decoding on the first code stream to Get multiple consecutive 0-value distance measurement data.
  • the method further includes: performing entropy decoding on the first code stream to obtain distance measurement data except for the plurality of consecutive zero-valued distance measurement data.
  • the method further includes: acquiring a second code stream corresponding to the signal strength measurement data according to the code stream; decompressing the second code stream to obtain the signal strength measurement data.
  • the method further includes: acquiring a third code stream corresponding to the time measurement data according to the code stream; and decompressing the third code stream to obtain the time measurement data.
  • the method further includes: acquiring a fourth code stream corresponding to the rotation angle measurement data according to the code stream; decompressing the fourth code stream to obtain the rotation angle measurement data data.
  • the decompression includes run-length decoding, entropy decoding, or residual-based entropy decoding.
  • the present application provides a decompression device, including: an acquisition module, configured to acquire a code stream; acquire a first code stream corresponding to distance measurement data according to the code stream; a decompression module, configured to perform the The first code stream is decompressed to obtain the distance measurement data; and the signal strength measurement data is obtained according to the distance measurement data.
  • the decompression module is specifically configured to set the signal strength measurement data corresponding to the distance measurement data to 0 when the distance measurement data is 0.
  • the decompression module is specifically configured to calculate the signal strength measurement data corresponding to the distance measurement data only according to the distance measurement data when the distance measurement data is non-zero ; or, when the distance measurement data is non-zero, only calculate the signal strength measurement data corresponding to the distance measurement data according to the decoded one or more non-zero distance measurement data.
  • the decompression module is specifically configured to obtain a second code stream corresponding to the signal strength measurement data according to the code stream; decompress the second code stream to obtain A residual of the signal strength measurement data of 0; the non-zero signal strength measurement data is obtained according to the decoded one or more non-zero distance measurement data and the residual.
  • the obtaining module is further configured to obtain a second code stream corresponding to the signal strength measurement data according to the code stream;
  • the binary stream is decompressed to obtain non-zero signal strength measurement data.
  • the decompression module is specifically configured to predict and obtain non-zero signal strength measurement data according to the decoded one or more non-zero distance measurement data; The strength measurement predicts another non-zero signal strength measurement.
  • the decompression module is specifically configured to perform run-length decoding on the first code stream to obtain a plurality of continuous zero-value distance measurement data.
  • the decompression module is specifically configured to perform entropy decoding on the first code stream to obtain distance measurement data except for the plurality of consecutive zero-value distance measurement data.
  • the obtaining module is further configured to obtain a third code stream corresponding to the time measurement data according to the code stream; Decompression is performed to obtain the time measurement data.
  • the acquiring module is further configured to acquire a fourth code stream corresponding to the rotation angle measurement data according to the code stream; The stream is decompressed to obtain the rotational angle measurement data.
  • the decompression includes run-length decoding, entropy decoding, or residual-based entropy decoding.
  • the present application provides a compression device, including: an acquisition module, configured to acquire data to be compressed, the data to be compressed is the measurement data collected by laser radar N times, N ⁇ 1; a compression module, used When the data to be compressed includes corresponding distance measurement data and signal strength measurement data, compress the distance measurement data to obtain a first code stream; perform the signal strength measurement data according to the distance measurement data Predict to get the second code stream.
  • an acquisition module configured to acquire data to be compressed, the data to be compressed is the measurement data collected by laser radar N times, N ⁇ 1
  • a compression module used When the data to be compressed includes corresponding distance measurement data and signal strength measurement data, compress the distance measurement data to obtain a first code stream; perform the signal strength measurement data according to the distance measurement data Predict to get the second code stream.
  • the compression module is specifically configured to, when the distance measurement data is non-zero, pair the signal strength corresponding to the distance measurement data according to one or more non-zero distance measurement data
  • the measurement data is predicted to obtain the residual of the signal strength measurement data; and the second code stream is obtained according to the residual of the signal strength measurement data.
  • the compression module is specifically configured to, when the data to be compressed includes a plurality of non-zero distance measurement data, The signal strength measurement data is subjected to residual-based entropy coding to obtain the second code stream.
  • the compression module is specifically configured to obtain non-zero distance measurement data; according to one or more non-zero distance measurement data, the non-zero Predict the signal strength measurement data of the non-zero signal strength measurement data to obtain the first residual error; predict another non-zero signal strength measurement data according to the non-zero signal strength measurement data to obtain the second residual error; according to the first residual error and The second residual obtains the second code stream.
  • the compression module is specifically configured to, when the data to be compressed includes multiple zero-value distance measurement data, perform run-length encoding on the multiple zero-value distance measurement data to obtain the A first code stream; or, when the data to be compressed includes a plurality of non-zero distance measurement data, performing residual-based entropy encoding on the plurality of non-zero distance measurement data to obtain the first code stream .
  • the compression module is further configured to, when the data to be compressed further includes time measurement data, perform run-length coding, entropy coding, or residual-based entropy coding on the time measurement data to Obtain a third code stream; and/or, when the data to be compressed further includes rotation angle measurement data, perform run-length coding, entropy coding, or residual-based entropy coding on the rotation angle measurement data to obtain a fourth code stream .
  • the present application provides a compression device, including: an acquisition module, configured to acquire data to be compressed, the data to be compressed is the measurement data collected by laser radar N times, N ⁇ 1; a compression module, used When the data to be compressed includes a plurality of zero-value distance measurement data, run-length encoding is performed on the plurality of zero-value distance measurement data to obtain a first code stream.
  • the compression module is further configured to, when the data to be compressed includes multiple non-zero distance measurement data, perform residual error-based entropy encoding to obtain the first code stream.
  • the compression module is further configured to, when the data to be compressed further includes signal strength measurement data, perform run-length coding, entropy coding, or residual-based entropy coding to obtain a second code stream; and/or, when the data to be compressed further includes time measurement data, perform run-length coding, entropy coding or residual-based entropy coding on the time measurement data to obtain a third code stream; and/or, when the data to be compressed further includes rotation angle measurement data, run-length coding, entropy coding or residual-based entropy coding is performed on the rotation angle measurement data to obtain a fourth code stream.
  • the present application provides a decompression device, including: an acquisition module, configured to acquire a code stream; acquire a first code stream corresponding to distance measurement data according to the code stream; a decompression module, configured to perform the Run-length decoding is performed on the first code stream to obtain a plurality of continuous 0-value distance measurement data.
  • the decompression module is further configured to perform entropy decoding on the first code stream to obtain distance measurement data except for the plurality of consecutive zero-valued distance measurement data.
  • the obtaining module is further configured to obtain a second code stream corresponding to the signal strength measurement data according to the code stream; The stream is decompressed to obtain the signal strength measurement data.
  • the obtaining module is further configured to obtain a third code stream corresponding to the time measurement data according to the code stream; Decompression is performed to obtain the time measurement data.
  • the acquiring module is further configured to acquire a fourth code stream corresponding to the rotation angle measurement data according to the code stream; The stream is decompressed to obtain the rotational angle measurement data.
  • the decompression includes run-length decoding, entropy decoding, or residual-based entropy decoding.
  • the present application provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors , so that the one or more processors implement the method described in any one of the first to fourth aspects above.
  • the present application provides a computer-readable storage medium, including a computer program.
  • the computer program When the computer program is executed on a computer, the computer executes the method described in any one of the first to fourth aspects above.
  • the present application provides a computer program product, the computer program product includes computer program code, and when the computer program code is run on a computer, it is used to execute any one of the above first to fourth aspects. described method.
  • FIG. 1 is an exemplary block diagram of a codec system 10 according to an embodiment of the present application
  • FIG. 2 is a flowchart of a process 200 of a method for compressing radar data according to an embodiment of the present application
  • Figure 3a is a schematic diagram of the format of UDP data in the embodiment of the present application.
  • FIG. 3b is a schematic diagram of the format of packet data in the embodiment of the present application.
  • Fig. 3c is a schematic diagram of a two-dimensional matrix of an embodiment of the present application.
  • FIG. 4 is a flowchart of a process 400 of a method for decompressing radar data according to an embodiment of the present application
  • Fig. 5 is a flow chart of the suitability of the radar data compression/decompression method according to the embodiment of the present application.
  • FIG. 6 is an exemplary structural schematic diagram of a compression device 600 according to an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of an exemplary decompression apparatus 700 according to an embodiment of the present application.
  • At least one (item) means one or more, and “multiple” means two or more.
  • “And/or” is used to describe the association relationship of associated objects, indicating that there can be three types of relationships, for example, “A and/or B” can mean: only A exists, only B exists, and A and B exist at the same time , where A and B can be singular or plural.
  • the character “/” generally indicates that the contextual objects are an “or” relationship.
  • At least one of the following” or similar expressions refer to any combination of these items, including any combination of single or plural items.
  • At least one item (piece) of a, b or c can mean: a, b, c, "a and b", “a and c", “b and c", or "a and b and c ", where a, b, c can be single or multiple.
  • Raw data of lidar refers to user datagram protocol (UDP) packets or packet data.
  • UDP user datagram protocol
  • UDP data Network transmission data based on UDP protocol.
  • the direct output data of lidar is transmitted externally in the form of UDP data.
  • the packet data can be obtained by parsing and sorting the UDP data.
  • Packet data the storage form of the original data of lidar, which can be obtained by organizing UDP data packets.
  • the packet data includes information such as distance, signal strength, rotation angle, laser identification (identity, ID) and time stamp of each point. Packet data usually contains information transmitted multiple times.
  • the grouped data can be directly input into the calibration algorithm, and the point cloud is obtained after calibration. The aforementioned calibration process is irreversible.
  • Point cloud is a data set of points in a certain coordinate system. It contains a wealth of information, for example, the three-dimensional coordinates (X, Y, Z), color, classification value, intensity value and other information of the point. Using dense point clouds can restore the real world in three dimensions. LiDAR point clouds are usually calibrated from grouped data.
  • One shot The process in which the lidar emits all lasers and receives reflections is called one shot.
  • the number of points that can be obtained by one launch is related to the number of radar lines. For example, one launch of a 32-line radar can theoretically launch 32 lasers, receive 32 reflections, and get 32 points in total.
  • the compression methods for lidar data are roughly divided into two categories, one is to compress the point cloud, and the other is to compress the original data of the lidar, that is, UDP data packets or packet data.
  • the algorithms for compressing the original data of the lidar mostly use the working principle and data format of the radar to arrange the grouped data into a 2D matrix, and then use the image compression method to compress.
  • the matrixed grouped data and the pixel data of the image still have very different distributions. Therefore, many algorithms in the image encoding and decoding technology are not suitable for the matrixed grouped data.
  • an embodiment of the present application provides a method for compressing radar data, so as to realize efficient compression of lidar data.
  • FIG. 1 is an exemplary block diagram of a codec system 10 according to an embodiment of the present application.
  • the compressor 12 and the decompressor 16 in the codec system 10 represent devices and the like that can be used to implement various techniques according to various examples described in the embodiments of the present application.
  • the codec system 10 includes an encoding end and a decoding end, the encoding end is used to compress the radar data, and provides the code stream obtained after compression to the decoding end, and the decoding end decompresses the code stream to obtain the reconstructed radar data .
  • the encoding end includes a compressor 12 , and optionally, a data source 11 and a communication interface 13 .
  • the data source 11 may comprise or be any type of lidar for acquiring measurement data.
  • Data source 11 may also include or be any type of memory or storage.
  • the data output by the data source 11 is the original data of the radar, and the original data may be UDP data or packet data processed by packet.
  • the compressor 12 is used to receive the original data, and compress the original data to obtain a code stream.
  • the communication interface 13 can be used to receive the code stream, and send the code stream to the decoding end through the communication channel 14 .
  • the decoding end includes a decompressor 16 , and optionally, a communication interface 15 and a post-processor 17 .
  • the communication interface 15 is used to directly receive the code stream from the encoding end or from any other device such as a storage device, and provide the code stream to the decompressor 16 .
  • the communication interface 13 and the communication interface 15 can be used to pass through a direct communication link between the encoding end and the decoding end, such as a direct wired or wireless connection, or through any type of network, such as a wired network, a wireless network, or any combination thereof, Any type of private network and public network or any combination thereof, send or receive code streams.
  • Both the communication interface 13 and the communication interface 15 can be configured as a one-way communication interface indicated by the arrow pointing to the corresponding communication channel 14 of the decoding end from the encoding end in Fig. 1, or a two-way communication interface, and can be used for sending and receiving messages, etc., to Establishing a connection, confirming and exchanging any other information related to the communication link and/or data transmission such as encoded image data transmission, etc.
  • the post-processor 17 is used to post-process the decoded data (also referred to as reconstructed radar data) to obtain post-processed radar data.
  • the post-processing performed by the post-processor 17 may include, for example, conversion of UDP data/packet data into point clouds.
  • FIG. 1 shows the encoding end and the decoding end as independent devices
  • the device embodiment may also include the encoding device and the decoding device or the functions of encoding and decoding at the same time, that is, include the encoding end or the corresponding function and the decoding end or corresponding function.
  • the encoding end or corresponding function and the decoding end or corresponding function may be implemented using the same hardware and/or software or by separate hardware and/or software or any combination thereof.
  • the encoding side and decoding side can comprise any of a variety of devices, including any type of handheld or stationary device, such as a notebook or laptop computer, smartphone, tablet or tablet computer, desktop computer, etc., And can use no or any type of operating system.
  • the encoding end and decoding end may be equipped with components for wireless communication. Therefore, the encoding end and the decoding end may be wireless communication devices.
  • the encoding and decoding system 10 shown in FIG. 1 is only exemplary. In some cases, the encoding end and the decoding end can be applied to the same device or different devices. Not specifically limited.
  • FIG. 2 is a flow chart of a process 200 of a method for compressing radar data according to an embodiment of the present application.
  • the process 200 can be executed by the encoding end (especially the compressor 12 ) in the codec system 10 .
  • the process 200 is described as a series of steps or operations. It should be understood that the process 200 may be executed in various orders and/or concurrently, and is not limited to the execution order shown in FIG. 2 . Assuming that the measurement data collected by the laser radar for N transmissions is using the codec system 10 , the process 200 including the following steps is executed to compress the above measurement data.
  • Process 200 may include:
  • Step 201 Obtain data to be compressed.
  • the raw data of lidar refers to user datagram protocol (UDP) data packets or packet data.
  • Fig. 3a is a schematic diagram of the format of the UDP data of the embodiment of the present application.
  • the UDP data includes additional information and measurement data, wherein the additional information includes global positioning system (global positioning system, GPS) time stamp, echo and Coordinated universal time (universal time coordinated, UTC), used to obtain time information;
  • measurement data includes 10 data blocks (block), a block corresponds to a laser radar launch, each launch can get 1 rotation angle (azimuth angle) Measurement data and 40 (indicates that 40-line lidar has 40 laser emitting modules) channel unit measurement data.
  • Figure 3b is a schematic diagram of the format of the packet data in the embodiment of the present application. As shown in Figure 3b, the UDP data shown in Figure 3a is sorted out to obtain the packet data. In the packet data, there is still a block corresponding to one emission of the lidar, and a block Including 1 azimuth angle measurement data and 40 sets of distance measurement data and signal strength measurement data, it can be seen that there is a one-to-one correspondence between the distance measurement data and the signal strength measurement data.
  • the data to be compressed in the embodiment of the present application may refer to the above-mentioned UDP data or the above-mentioned packet data, but for the convenience of subsequent steps, if the data to be compressed is UDP data, the UDP data can now be sorted into packet data , and then perform the next steps.
  • FIG. 3c is a schematic diagram of a two-dimensional matrix according to an embodiment of the present application.
  • one row of the matrix corresponds to one laser of the lidar
  • one column of the matrix corresponds to one emission of the lidar.
  • the lidar has a total of 40 lines (laser), and uses 10 shots.
  • the distance measurement data is expressed as a matrix, then the matrix is a 40 ⁇ 10 two-dimensional matrix.
  • Each element in the matrix corresponds to a distance measurement data, and when the encoding end compresses the distance measurement data, it can sequentially extract elements from the matrix according to the direction of the arrow shown in Figure 3c.
  • FIG. 3c illustrates the correspondence between the two-dimensional matrix and the measurement data by taking the distance measurement data as an example.
  • the signal strength measurement data in the data to be compressed can also be expressed as a two-dimensional matrix by the same method, and the same or Different order to extract elements from matrix.
  • Step 202 When the data to be compressed includes corresponding distance measurement data and signal strength measurement data, compress the distance measurement data to obtain a first code stream.
  • the compression methods that can be used by the encoding end include:
  • run-length encoding is performed on the multiple zero-value distance measurement data to obtain the first code stream.
  • the data to be compressed may include multiple distance measurement data.
  • the multiple distance measurement data are arranged in the form of a binary matrix, there may be continuous distance measurements with multiple 0 values according to the extraction order of the matrix elements. data, at this time, it may be considered to perform run-length encoding on these 0-value distance measurement data.
  • 0 means that the value of the distance measurement data is 0, and 10 means that there are 10 0-value distance measurement data; or, you can use Its compressed expression is "0, (0,9), 10", 0 indicates that the value of the distance measurement data is 0, (0,9) indicates the position of the first 0 value in this group of 0-value distance measurement data, and 10 Indicates that there are 10 0-value distance measurement data; or, it can be compressed and expressed as "0, (0,9), (9,9)", 0 means that the value of the distance measurement data is 0, and (0,9) means The position of this group of 0 values from the first 0 value in the measurement data, (9,9) indicates the position of this group of 0 values from the last 0 value in the measurement data.
  • run-length coding can greatly reduce the amount of compressed data, the more continuous 0-value distance measurement data, the higher the compression efficiency. It should be noted that other run-length encoding methods may also be used to compress multiple zero-valued distance measurement data, which is not specifically limited in this embodiment of the present application.
  • the data to be compressed includes multiple non-zero distance measurement data
  • residual-based entropy coding is performed on the multiple non-zero distance measurement data to obtain the first code stream.
  • the non-zero value distance measurement data can be sequentially extracted according to the element extraction sequence shown in Figure 3c, and the residual error of two adjacent non-zero value distance measurement data can be calculated, and then based on the residual error Entropy coding.
  • entropy coding based on residual error is performed on multiple non-zero distance measurement data, and other implementation methods can also be used for calculation, for example, calculation based on previous multiple non-zero distance measurement data
  • the current non-zero value is the residual of the distance measurement data, which is not specifically limited in this embodiment of the present application.
  • Step 203 Predict the signal strength measurement data according to the distance measurement data to obtain a second code stream.
  • the distance measurement data and the signal strength measurement data in the same group are also correlated.
  • the corresponding signal strength measurement data is also 0. This is because when the distance measurement data is 0, it means that the signal is too weak to obtain valid information, so ignore the distance measurement data and mark it as 0, and the corresponding The signal strength measurement data of is also recorded as 0. Therefore, cross-modal compression of the two can be considered to achieve optimal compression efficiency.
  • the encoding end may not process it, that is, The encoding end gives up the processing of the signal strength measurement data corresponding to the 0-value distance measurement data, and no position will be reserved for such signal strength measurement data in the code stream.
  • the encoding end compresses the data to be compressed, the signal strength measurement data corresponding to the 0-value distance measurement data can be regarded as non-existent, which can greatly reduce the amount of data. The more 0-values, the higher the compression efficiency, thereby saving the occupied space. stream.
  • the above-mentioned method can also be referred to, that is, the encoding end abandons the processing of the signal strength measurement data that has a calculation relationship with the distance measurement data, and no position will be reserved for such signal strength measurement data in the code stream.
  • the signal strength measurement data that has a computational relationship with the distance measurement data can be regarded as non-existent, which can greatly reduce the amount of data.
  • the more signal strength measurement data that has a computational relationship with the distance measurement data The higher the compression efficiency is, the more code streams are saved.
  • the distance measurement data when the distance measurement data is non-zero, predict the signal strength measurement data corresponding to the distance measurement data according to one or more non-zero distance measurement data to obtain the residual signal strength measurement data difference, and then obtain the second code stream according to the residual of the signal strength measurement data.
  • the signal strength measurement data can be predicted according to the distance measurement data in the same group, for example, calculate the residual of the distance measurement data and the signal strength measurement data in the same group, and use the residual as the residual of the signal strength measurement data
  • the difference is entropy encoded.
  • the signal strength measurement data can be predicted based on the distance measurement data in the same group and the distance measurement data before the distance measurement data, for example, calculate the intermediate value according to the aforementioned multiple distance measurement data, and then calculate the intermediate value and the residual of the signal strength measurement data, and the residual is used as the residual of the signal strength measurement data for entropy encoding.
  • the data to be compressed includes a plurality of non-zero distance measurement data
  • residual-based entropy coding is performed on the plurality of signal strength measurement data corresponding to the plurality of non-zero distance measurement data to Get the second code stream.
  • the encoding end can use the method for compressing the non-zero distance measurement data to compress the non-zero signal strength measurement data, that is,
  • the non-zero value signal strength measurement data can be sequentially extracted according to the element extraction sequence shown in Figure 3c, and the residual error of two adjacent non-zero value signal strength measurement data can be calculated, and then based on The residuals are entropy coded.
  • entropy coding based on residual error is performed on multiple non-zero signal strength measurement data, and other implementation methods can also be used for calculation, for example, based on previous multiple non-zero signal strength measurement data
  • the data calculates the residual of the current non-zero signal strength measurement data, which is not specifically limited in this embodiment of the present application.
  • the non-zero distance measurement data is acquired, and the non-zero signal strength measurement data corresponding to the non-zero distance measurement data is predicted according to one or more non-zero distance measurement data to obtain the first
  • the residual error is to predict another non-zero signal strength measurement data according to the non-zero signal strength measurement data to obtain a second residual error, and finally obtain a second code stream according to the first residual error and the second residual error.
  • the embodiment of the present application is a combination of the above two methods, that is, among multiple non-zero signal strength measurement data, a part of non-zero signal strength measurement data can be predicted based on one or more non-zero distance measurement data to obtain its residual error value (the first residual), another part of the non-zero signal strength measurement data can be predicted based on the compressed, non-zero signal strength measurement data in the front to obtain its residual value (second residual), and finally Entropy coding is performed on the first residual and the second residual above.
  • the first non-zero signal strength measurement data can be predicted to obtain its residual value based on the corresponding non-zero distance measurement data, and the non-first non-zero signal strength measurement data can be based on the preceding Non-zero signal strength measurement data is predicted to get its residual value.
  • distance measurement data and signal strength measurement data in the same group are correlated, so based on this correlation, this embodiment of the present application can also use other methods to compress distance measurement data and signal strength across modalities. Intensity measurement data, without specific limitation.
  • the encoding end can also compress the time measurement data and the rotation angle measurement data in the data to be compressed:
  • the data to be compressed further includes time measurement data, perform run-length coding, entropy coding, or residual-based entropy coding on the time measurement data to obtain a third code stream; and/or,
  • the data to be compressed further includes rotation angle measurement data
  • run-length coding, entropy coding or residual-based entropy coding is performed on the rotation angle measurement data to obtain a fourth code stream.
  • run-length coding For specific implementations of run-length coding, entropy coding, or residual-based entropy coding, reference may be made to the above-mentioned coding method of distance measurement data, which will not be repeated here.
  • the two are compressed in a cross-modal manner, so that the amount of data to be compressed is greatly reduced, especially 0
  • FIG. 4 is a flow chart of a process 400 of a method for decompressing radar data according to an embodiment of the present application.
  • the process 400 can be executed by the decoding end (especially the decompressor 16 ) in the codec system 10 .
  • the process 400 is described as a series of steps or operations. It should be understood that the process 400 may be executed in various orders and/or concurrently, and is not limited to the execution order shown in FIG. 4 . Assuming that the codec is using the codec system 10 , the process 400 including the following steps is performed to decompress the above codec.
  • Process 400 may include:
  • Step 401 acquire code stream.
  • the code stream includes at least one of the first code stream, the second code stream, the third code stream and the fourth code stream.
  • it is a solution in the city, mainly considering distance measurement data and signal strength measurement data, so in the code stream Need to include at least the first code stream.
  • Step 402. Acquire a first code stream corresponding to the distance measurement data according to the code stream.
  • some syntax elements may be carried in the code stream to indicate the meanings of different fields in the code stream. For example, put a label on the beginning of the first code stream to remind the decoding end that the code streams after this point are all the first code streams corresponding to the distance measurement data; The starting position (byte or bit, etc.).
  • the encoding end and the decoding end may pre-agree on the lengths of different code streams, for example, the first code stream corresponding to the distance measurement data, the second code stream corresponding to the signal strength measurement data, the second code stream corresponding to the time measurement data
  • the third code stream, the fourth code stream corresponding to the rotation angle measurement data pre-set the length of these four code streams to be the same, and the order has been determined, then the decoder can use the bytes or bits of the currently parsed code stream Determine which code stream among the preceding four.
  • Step 403 decompress the first code stream to obtain distance measurement data.
  • run-length decoding is performed on the first code stream to obtain a plurality of continuous zero-valued distance measurement data.
  • the decoding end can perform run-length decoding on the corresponding fields in the code stream according to the instruction of the syntax element, so as to obtain a plurality of continuous zero-value distance measurement data.
  • entropy decoding is performed on the first code stream to obtain distance measurement data except a plurality of consecutive zero-value distance measurement data.
  • the decoding end can use entropy decoding on the corresponding fields in the code stream to obtain the residual of the distance measurement data according to the instruction of the syntax element, and then use the opposite calculation method to obtain the corresponding distance measurement Data, for example, the current distance measurement data is obtained by adding the aforementioned residual error to the previous distance measurement data, and for example, calculating the median value based on the obtained multiple distance measurement data, and adding the aforementioned residual error to the median value It is the current distance measurement data.
  • Step 404 acquire signal strength measurement data according to the distance measurement data.
  • the signal strength measurement data corresponding to the distance measurement data is set to 0.
  • the decoder can directly convert the decompressed 0-value distance measurement data to The corresponding signal strength measurement data is set to 0 instead of being decompressed from the code stream, which can save the occupied code stream and greatly reduce the amount of data.
  • the signal strength measurement data corresponding to the distance measurement data is calculated only according to the distance measurement data; or, when the distance measurement data is non-zero, only according to the already
  • the one or more non-zero distance measurement data obtained by decoding are calculated to obtain signal strength measurement data corresponding to the distance measurement data.
  • the second code stream corresponding to the signal strength measurement data is obtained according to the code stream, and the second code stream is decompressed to obtain a non-zero residual of the signal strength measurement data.
  • One or more non-zero distance measurements and residuals result in non-zero signal strength measurements.
  • the signal strength measurement data can be predicted based on the distance measurement data in the same group, or can be predicted based on the distance measurement data in the same group and the distance measurement before the distance measurement data
  • the data jointly predict the signal strength measurement data.
  • the decoder can obtain the residual error of the signal strength measurement data by analyzing the code stream, and then obtain the signal strength measurement data through the residual error and the corresponding distance measurement data according to the prediction method.
  • a second code stream corresponding to the signal strength measurement data is acquired according to the code stream, and the second code stream is decompressed to obtain non-zero signal strength measurement data.
  • the decoding end may decompress the non-zero signal strength measurement data by referring to the decompression method for the non-zero distance measurement data.
  • the non-zero signal strength measurement data is predicted according to the decoded one or more non-zero distance measurement data, and another non-zero signal is predicted according to the non-zero signal strength measurement data Strength measurement data.
  • this embodiment of the present application is a combination of the above two methods, that is, among multiple non-zero signal strength measurement data, a part of non-zero signal strength measurement data can be based on one or A plurality of non-zero distance measurement data is predicted to obtain its residual value (first residual), and another part of non-zero signal strength measurement data can be predicted based on the compressed, non-zero signal strength measurement data in the front
  • the residual value (the second residual)
  • the signal strength measurement data can be obtained through the residual and the corresponding distance measurement data in a predictive manner.
  • the decoding end may also obtain a third code stream corresponding to the time measurement data according to the code stream, and decompress the third code stream to obtain the time measurement data.
  • a fourth code stream corresponding to the rotation angle measurement data may also be acquired according to the code stream, and the fourth code stream may be decompressed to obtain the rotation angle measurement data.
  • the processing at the decoding end corresponds to that at the encoding end, and will not be repeated here.
  • the two are decompressed in a cross-modal manner, because part of the signal strength measurement data in the code stream can save Its bit stream greatly reduces the code stream length and improves compression efficiency.
  • the embodiment of the present application also provides a radar data compression/decompression method, that is, the encoding end obtains the data to be compressed, and the data to be compressed is the measurement data collected from N times of laser radar transmission, N ⁇ 1.
  • the data to be compressed includes multiple zero-value distance measurement data
  • run-length encoding is performed on the multiple zero-value distance measurement data to obtain a first code stream.
  • the decoding end acquires a code stream, and obtains a first code stream corresponding to the distance measurement data according to the code stream; and performs run-length decoding on the first code stream to obtain a plurality of continuous 0-value distance measurement data.
  • run-length coding can be used, which can greatly reduce the amount of data and improve compression efficiency.
  • the decoding end can obtain the distance measurement data by means of run-length decoding for part of the code stream, which can also improve the efficiency of decompression.
  • FIG. 5 is a flow chart of the suitability of the radar data compression/decompression method of the embodiment of the present application.
  • the data includes time measurement data, rotation angle measurement data, distance measurement data and signal strength measurement data (intensity).
  • the compressor at the encoding end can include multiple encoding modules.
  • the time encoding module compresses the time measurement data, and the rotation angle encoding module
  • the rotation angle measurement data is compressed, and the distance coding module and the signal strength coding module sequentially compress the time measurement data and the signal strength measurement data.
  • the compressed data is transmitted to the decoder.
  • the decompressor at the decoding end also includes multiple decoding modules.
  • the time decoding module decompresses the code stream corresponding to the time measurement data to obtain the time measurement data
  • the rotation angle decoding module decompresses the code stream corresponding to the rotation angle measurement data to obtain
  • the rotation angle measurement data, the distance decoding module and the signal strength decoding module sequentially decompress the code stream corresponding to the distance measurement data and the code stream corresponding to the signal strength measurement data to obtain distance measurement data and signal strength measurement data.
  • the grouped data reconstructed from the time measurement data, the rotation angle measurement data, the distance measurement data and the signal strength measurement data are transformed into a point cloud.
  • FIG. 6 is a schematic structural diagram of an exemplary compression device 600 according to an embodiment of the present application. As shown in FIG. 6 , the device 600 of this embodiment may be applied to the compressor 12 .
  • the apparatus 600 may include: an acquisition module 601 and a compression module 602 . in,
  • the obtaining module 601 is used to obtain the data to be compressed, the data to be compressed is the measurement data collected by N times of laser radar emission, N ⁇ 1; the compression module 602 is used to obtain the data to be compressed when the data to be compressed includes the corresponding distance When measuring data and signal strength measurement data, compress the distance measurement data to obtain a first code stream; predict the signal strength measurement data according to the distance measurement data to obtain a second code stream.
  • the compression module 602 is specifically configured to pair the signal corresponding to the distance measurement data according to one or more non-zero distance measurement data when the distance measurement data is non-zero
  • the strength measurement data is predicted to obtain a residual of the signal strength measurement data; and the second code stream is obtained according to the residual of the signal strength measurement data.
  • the compression module 602 is specifically configured to, when the data to be compressed includes a plurality of non-zero distance measurement data, performing residual-based entropy coding on the signal strength measurement data to obtain the second code stream.
  • the compression module 602 is specifically configured to obtain non-zero distance measurement data; according to one or more non-zero distance measurement data, the non-zero Predicting the signal strength measurement data of 0 to obtain a first residual error; predicting another non-zero signal strength measurement data according to the non-zero signal strength measurement data to obtain a second residual error; according to the first residual error and the second residual to obtain the second code stream.
  • the compression module 602 is specifically configured to, when the data to be compressed includes multiple zero-value distance measurement data, perform run-length encoding on the multiple zero-value distance measurement data to obtain the the first code stream; or, when the data to be compressed includes a plurality of non-zero distance measurement data, perform residual-based entropy coding on the plurality of non-zero distance measurement data to obtain the first code flow.
  • the compression module 602 is further configured to perform run-length coding, entropy coding, or residual-based entropy coding on the time measurement data when the data to be compressed also includes time measurement data to obtain a third code stream; and/or, when the data to be compressed further includes rotation angle measurement data, perform run-length coding, entropy coding, or residual-based entropy coding on the rotation angle measurement data to obtain a fourth code stream flow.
  • the obtaining module 601 is used to obtain the data to be compressed
  • the data to be compressed is the measurement data collected by N times of laser radar transmission, N ⁇ 1
  • the compression module 602 is used to When the data to be compressed includes a plurality of zero-value distance measurement data, run-length encoding is performed on the plurality of zero-value distance measurement data to obtain a first code stream.
  • the compression module 602 is further configured to, when the data to be compressed includes a plurality of non-zero distance measurement data, perform residual error-based Entropy encoding of to obtain the first code stream.
  • the compression module 602 is further configured to, when the data to be compressed further includes signal strength measurement data, perform run-length coding, entropy coding, or residual-based entropy coding to obtain the second code stream; and/or, when the data to be compressed further includes time measurement data, run-length coding, entropy coding or residual-based entropy coding is performed on the time measurement data to obtain the third bit stream code stream; and/or, when the data to be compressed further includes rotation angle measurement data, perform run-length coding, entropy coding or residual-based entropy coding on the rotation angle measurement data to obtain a fourth code stream.
  • FIG. 7 is a schematic structural diagram of an exemplary decompression apparatus 700 according to an embodiment of the present application. As shown in FIG. 7 , the apparatus 700 of this embodiment may be applied to the decompressor 16 .
  • the apparatus 700 may include: an acquisition module 701 and a decompression module 702 . in,
  • the obtaining module 701 is used to obtain the code stream; according to the code stream, the first code stream corresponding to the distance measurement data is obtained; the decompression module 702 is used to decompress the first code stream to obtain the distance measurement data; acquiring signal strength measurement data according to the distance measurement data.
  • the decompression module 702 is specifically configured to set the signal strength measurement data corresponding to the distance measurement data to 0 when the distance measurement data is 0.
  • the decompression module 702 is specifically configured to calculate the signal strength measurement corresponding to the distance measurement data only according to the distance measurement data when the distance measurement data is non-zero or, when the distance measurement data is non-zero, only calculate the signal strength measurement data corresponding to the distance measurement data according to one or more non-zero distance measurement data obtained through decoding.
  • the decompression module 702 is specifically configured to obtain a second code stream corresponding to the signal strength measurement data according to the code stream; decompress the second code stream to obtain A residual of the non-zero signal strength measurement data; the non-zero signal strength measurement data is obtained according to the decoded one or more non-zero distance measurement data and the residual.
  • the acquiring module 701 is further configured to acquire a second code stream corresponding to the signal strength measurement data according to the code stream; the decompression module 702 is further configured to The second code stream is decompressed to obtain non-zero signal strength measurement data.
  • the decompression module 702 is specifically configured to predict and obtain non-zero signal strength measurement data according to the decoded one or more non-zero distance measurement data; The signal strength measurement predicts another non-zero signal strength measurement.
  • the decompression module 702 is specifically configured to perform run-length decoding on the first code stream to obtain a plurality of continuous zero-value distance measurement data.
  • the decompression module 702 is specifically configured to perform entropy decoding on the first code stream to obtain distance measurement data except for the plurality of consecutive zero-valued distance measurement data.
  • the acquiring module 701 is further configured to acquire a third code stream corresponding to the time measurement data according to the code stream; the decompression module 702 is also configured to The code stream is decompressed to obtain the time measurement data.
  • the obtaining module 701 is further configured to obtain a fourth code stream corresponding to the rotation angle measurement data according to the code stream; The four-bit stream is decompressed to obtain the rotation angle measurement data.
  • the decompression includes run-length decoding, entropy decoding, or residual-based entropy decoding.
  • the acquisition module 701 is configured to acquire a code stream; obtain a first code stream corresponding to the distance measurement data according to the code stream; the decompression module 702 is configured to process the first code stream Run-length decoding is performed to obtain multiple consecutive 0-valued distance measurement data.
  • the decompression module 702 is further configured to perform entropy decoding on the first code stream to obtain distance measurement data except for the plurality of consecutive zero-value distance measurement data.
  • the acquiring module 701 is further configured to acquire a second code stream corresponding to the signal strength measurement data according to the code stream; the decompression module 702 is further configured to The binary stream is decompressed to obtain the signal strength measurement data.
  • the acquiring module 701 is further configured to acquire a third code stream corresponding to the time measurement data according to the code stream; the decompression module 702 is also configured to The code stream is decompressed to obtain the time measurement data.
  • the obtaining module 701 is further configured to obtain a fourth code stream corresponding to the rotation angle measurement data according to the code stream; The four-bit stream is decompressed to obtain the rotation angle measurement data.
  • the decompression includes run-length decoding, entropy decoding, or residual-based entropy decoding.
  • each step of the above-mentioned method embodiments may be completed by an integrated logic circuit of hardware in a processor or instructions in the form of software.
  • the processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other possible Program logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the steps of the methods disclosed in the embodiments of the present application may be directly implemented by a hardware coded processor, or executed by a combination of hardware and software modules in the coded processor.
  • the software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.
  • the memories mentioned in the above embodiments may be volatile memories or nonvolatile memories, or may include both volatile and nonvolatile memories.
  • the non-volatile memory can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically programmable Erases programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory can be random access memory (RAM), which acts as external cache memory.
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM direct memory bus random access memory
  • direct rambus RAM direct rambus RAM
  • the disclosed systems, devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (personal computer, server, or network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disc and other media that can store program codes. .

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

本申请提供一种雷达数据的压缩方法和装置。雷达数据的压缩方法包括：获取待压缩数据，待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；当待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对距离测量数据进行压缩以得到第一码流；根据距离测量数据对信号强度测量数据进行预测以得到第二码流。本申请可以使得待压缩的数据量大大减小，尤其是0值越多压缩效率越高，进而节省了占用的码流。

Description

雷达数据的压缩方法和装置
本申请要求于2021年11月23日提交中国专利局、申请号为202111393470.5、申请名称为“雷达数据的压缩方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及雷达技术，尤其涉及一种雷达数据的压缩方法和装置。
背景技术
通过激光雷达获取点云数据在自动驾驶的各个算法模块(例如定位，壁障等)有着重要作用。近年，随着激光雷达技术的演进，激光雷达的耐用性大幅提升，单价也下沉到市场可以接受的程度，各类车规级激光雷达层出不穷。如今激光雷达已成为4级自动驾驶的标配传感器，被诸多量产车型所采纳。
与此同时，激光雷达的数据传输愈逐成为一大痛点。随着对高分辨点云的追求，激光雷达的线数不断提高，传输单个激光雷达的原始数据可能需要100Mbps以上的带宽。而出于安全性和可靠性的考虑，自动驾驶车辆通常还有搭配有摄像头、毫米波雷达等多种传感器，进而对数据的传输和存储带来更大的压力。
可见，为了能满足激光雷达数据在传输和存储方面的需求，高效的压缩算法不可或缺。
发明内容
本申请提供一种雷达数据的压缩方法和装置，以使得待压缩的数据量大大减小，尤其是0值越多压缩效率越高，进而节省了占用的码流。
第一方面，本申请提供一种雷达数据的压缩方法，包括：获取码流；根据所述码流获取与距离测量数据对应的第一码流；对所述第一码流进行解压缩以得到所述距离测量数据；根据所述距离测量数据获取信号强度测量数据。
如上所述，激光雷达的原始数据是指用户数据包协议(user datagram protocol，UDP)数据包或分组数据。对UDP数据进行整理得到分组数据，分组数据中仍然是一个数据块(block)对应激光雷达的一次发射，一个block包括一个转动角(azimuth angle)测量数据和多组距离(distance)测量数据和信号强度(intensity)测量数据，距离测量数据和信号强度测量数据之间是一一对应的。
本申请实施例中，针对不同类型的测量数据可以采用不同的压缩方式，其中，针对距离测量数据，编码端可以采用的压缩方法包括：
在一种可能的实现方式中，当待压缩数据包括多个0值距离测量数据时，对多个0值距离测量数据进行游程编码以得到第一码流。
在一种可能的实现方式中，当待压缩数据包括多个非0的距离测量数据时，对多个非0的距离测量数据进行基于残差的熵编码以得到第一码流。
同一组内的距离测量数据和信号强度测量数据之间具备对应关系，相应的，同一组内的距离测量数据和信号强度测量数据的取值也具备关联性，例如，距离测量数据为0时， 通常与其对应的信号强度测量数据也为0，这是因为距离测量数据为0时，表示信号很弱不足以得到有效信息，因此忽略该距离测量数据，将其标记为0，相应的其所对应的信号强度测量数据也记为0。因此可以考虑将二者进行跨模态压缩，以达到最优的压缩效率。
在一种可能的实现方式中，当距离测量数据为0时，其所对应的信号强度测量数据也为0，因此针对这一类的信号强度测量数据，编码端可以不对其进行处理，可以大大减小数据量，0值越多压缩效率越高，进而节省了占用的码流。
在一种可能的实现方式中，同一组内的距离测量数据和信号强度测量数据的取值，除了上述0值对应0值的关系外，还可能在二者之间具备计算关系，可以大大减小数据量，与距离测量数据具备计算关系的信号强度测量数据越多压缩效率越高，进而节省了占用的码流。
在一种可能的实现方式中，当距离测量数据为非0时，根据一个或多个非0的距离测量数据对与距离测量数据对应的信号强度测量数据进行预测以得到信号强度测量数据的残差，然后根据信号强度测量数据的残差获取第二码流。
在一种可能的实现方式中，当待压缩数据包括多个非0的距离测量数据时，对与多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到第二码流。
在一种可能的实现方式中，获取非0的距离测量数据，根据一个或多个非0的距离测量数据对与非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差，根据非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差，最后根据第一残差和第二残差获取第二码流。
本申请实施例，通过基于同一组内的距离测量数据和信号强度测量数据之间的关联性，采用跨模态的方式对二者进行压缩，使得待压缩的数据量大大减小，尤其是0值越多压缩效率越高，进而节省了占用的码流。
在一种可能的实现方式中，所述方法还包括：根据所述码流获取与时间测量数据对应的第三码流；对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述方法还包括：根据所述码流获取与转动角测量数据对应的第四码流；对所述第四码流进行解压缩以得到所述转动角测量数据。
上述解压缩包括游程解码、熵解码或者基于残差的熵解码。
第二方面，本申请提供一种雷达数据的压缩方法，包括：获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；当所述待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对所述距离测量数据进行压缩以得到第一码流；根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流。
码流包括第一码流、第二码流第三码流和第四码流中的至少一个，对于本申请是市里的方案，主要考虑距离测量数据和信号强度测量数据，因此码流中需要至少包括第一码流。可选的，码流中可以携带一些语法元素，用于指示码流中的不同字段的含义。可选的，编码端和解码端可以预先约定不同的码流的长度，根据当前解析到的码流的字节或比特位确定是前述四个中的哪个码流。
在一种可能的实现方式中，对第一码流进行游程解码以得到多个连续的0值距离测量数据。与编码端相对应，解码端可以根据语法元素的指示，对码流中的相应字段采用游程 解码，从而得到多个连续的0值距离测量数据。
在一种可能的实现方式中，对第一码流进行熵解码以得到除多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，当距离测量数据为0时，将与距离测量数据对应的信号强度测量数据置为0。
在一种可能的实现方式中，当距离测量数据为非0时，仅根据距离测量数据计算得到与距离测量数据对应的信号强度测量数据；或者，当距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与距离测量数据对应的信号强度测量数据。
在一种可能的实现方式中，根据码流获取与信号强度测量数据对应的第二码流，对第二码流进行解压缩得到非0的信号强度测量数据的残差，根据已解码得到的一个或多个非0的距离测量数据和残差得到非0的信号强度测量数据。
在一种可能的实现方式中，根据码流获取与信号强度测量数据对应的第二码流，对第二码流进行解压缩以得到非0的信号强度测量数据。
在一种可能的实现方式中，根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据，根据非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
本申请实施例，通过基于同一组内的距离测量数据和信号强度测量数据之间的关联性，采用跨模态的方式对二者进行解压缩，由于码流中针对部分信号强度测量数据可以节省其比特流，因此大大减小了码流长度，提高压缩效率。
在一种可能的实现方式中，所述方法还包括：当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
第三方面，本申请提供一种雷达数据的压缩方法，包括：获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到第一码流。
在一种可能的实现方式中，所述方法还包括：当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
在一种可能的实现方式中，所述方法还包括：当所述待压缩数据还包括信号强度测量数据时，对所述信号强度测量数据进行游程编码码、熵编码或者基于残差的熵编码以得到第二码流；和/或，当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
第四方面，本申请提供一种雷达数据的解压缩方法，包括：获取码流；根据所述码流获取与距离测量数据对应的第一码流；对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
在一种可能的实现方式中，所述方法还包括：对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，所述方法还包括：根据所述码流获取与信号强度测量数据对应的第二码流；对所述第二码流进行解压缩以得到所述信号强度测量数据。
在一种可能的实现方式中，所述方法还包括：根据所述码流获取与时间测量数据对应的第三码流；对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述方法还包括：根据所述码流获取与转动角测量数据对应的第四码流；对所述第四码流进行解压缩以得到所述转动角测量数据。
在一种可能的实现方式中，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
第五方面，本申请提供一种解压缩装置，包括：获取模块，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；解压缩模块，用于对所述第一码流进行解压缩以得到所述距离测量数据；根据所述距离测量数据获取信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块，具体用于当所述距离测量数据为0时，将与所述距离测量数据对应的信号强度测量数据置为0。
在一种可能的实现方式中，所述解压缩模块，具体用于当所述距离测量数据为非0时，仅根据所述距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据；或者，当所述距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块，具体用于根据所述码流获取与所述信号强度测量数据对应的第二码流；对所述第二码流进行解压缩得到非0的信号强度测量数据的残差；根据已解码得到的一个或多个非0的距离测量数据和所述残差得到所述非0的信号强度测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与所述信号强度测量数据对应的第二码流；所述解压缩模块，还用于对所述第二码流进行解压缩以得到非0的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块，具体用于根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据；根据所述非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块，具体用于对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
在一种可能的实现方式中，所述解压缩模块，具体用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与时间测量数据对应的第三码流；所述解压缩模块，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与转动角测量数据对应的第四码流；所述解压缩模块，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
在一种可能的实现方式中，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
第六方面，本申请提供一种压缩装置，包括：获取模块，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；压缩模块，用于当所述待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对所述距离测量数据进行压缩以得到第一码流；根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流。
在一种可能的实现方式中，所述压缩模块，具体用于当所述距离测量数据为非0时，根据一个或多个非0的距离测量数据对与所述距离测量数据对应的信号强度测量数据进行预测以得到所述信号强度测量数据的残差；根据所述信号强度测量数据的残差获取所述第二码流。
在一种可能的实现方式中，所述压缩模块，具体用于当所述待压缩数据包括多个非0的距离测量数据时，对与所述多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到所述第二码流。
在一种可能的实现方式中，所述压缩模块，具体用于获取非0的距离测量数据；根据一个或多个非0的距离测量数据对与所述非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差；根据所述非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差；根据所述第一残差和所述第二残差获取所述第二码流。
在一种可能的实现方式中，所述压缩模块，具体用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到所述第一码流；或者，当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
在一种可能的实现方式中，所述压缩模块，还用于当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
第七方面，本申请提供一种压缩装置，包括：获取模块，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；压缩模块，用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到第一码流。
在一种可能的实现方式中，所述压缩模块，还用于当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
在一种可能的实现方式中，所述压缩模块，还用于当所述待压缩数据还包括信号强度测量数据时，对所述信号强度测量数据进行游程编码码、熵编码或者基于残差的熵编码以得到第二码流；和/或，当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
第八方面，本申请提供一种解压缩装置，包括：获取模块，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；解压缩模块，用于对所述第一码流进行游程解 码以得到多个连续的0值距离测量数据。
在一种可能的实现方式中，所述解压缩模块，还用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与信号强度测量数据对应的第二码流；所述解压缩模块，还用于对所述第二码流进行解压缩以得到所述信号强度测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与时间测量数据对应的第三码流；所述解压缩模块，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述获取模块，还用于根据所述码流获取与转动角测量数据对应的第四码流；所述解压缩模块，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
在一种可能的实现方式中，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
第九方面，本申请提供一种电子设备，包括：一个或多个处理器；存储器，用于存储一个或多个程序；当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如上述第一至四方面中任一项所述的方法。
第十方面，本申请提供一种计算机可读存储介质，包括计算机程序，所述计算机程序在计算机上被执行时，使得所述计算机执行上述第一至四方面中任一项所述的方法。
第十一方面，本申请提供一种计算机程序产品，所述计算机程序产品包括计算机程序代码，当所述计算机程序代码在计算机上运行时，用于执行上述第一至四方面中任一项所述的方法。
附图说明
图1为本申请实施例的编解码系统10的示例性框图；
图2为本申请实施例的雷达数据的压缩方法的过程200的流程图；
图3a为本申请实施例的UDP数据的格式示意图；
图3b为本申请实施例的分组数据的格式示意图；
图3c为本申请实施例的二维矩阵的示意图；
图4为本申请实施例的雷达数据的解压缩方法的过程400的流程图；
图5为本申请实施例的雷达数据的压缩/解压缩方法的适宜性流程图；
图6为本申请实施例压缩装置600的一个示例性的结构示意图；
图7为本申请实施例解压缩装置700的一个示例性的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请中的附图，对本申请中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书实施例和权利要求书及附图中的术语“第一”、“第二”等仅用于区分描 述的目的，而不能理解为指示或暗示相对重要性，也不能理解为指示或暗示顺序。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元。方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
应当理解，在本申请中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和c”，其中a，b，c可以是单个，也可以是多个。
以下是本申请实施例涉及到的相关术语：
原始数据：激光雷达的原始数据是指用户数据包协议(user datagram protocol，UDP)数据包或分组数据。
UDP数据：基于UDP协议的网络传输数据。激光雷达的直接输出数据是以UDP数据的形式对外传输。对UDP数据进行解析和整理即可得到分组数据。
分组数据(packet data)：激光雷达的原始数据的存储形式，可以由UDP数据包整理得到。分组数据包含每个点的距离、信号强度、转动角、镭射标识(identity，ID)和时间戳等信息。分组数据通常包含多次发射的信息。分组数据可以被直接输入校准算法，经过校准后得到点云，前述校准过程不可逆。
点云(point cloud)：点云是某个坐标系下的点的数据集。其包含了丰富的信息，例如，点的三维坐标(X,Y,Z)、颜色、分类值、强度值等信息。使用密集的点云可以还原三维的现实世界。激光雷达的点云通常由分组数据经过校准得到。
一次发射：激光雷达发射所有镭射并接受到反射的过程称作一次发射。一次发射可以获得的点数和雷达的线数有关，例如，32线雷达的一次发射理论上可以发射32条镭射，接收32个反射，共得到32个点。
针对激光雷达的数据的压缩方法大致分为两个类，一类是压缩点云，另一类是压缩激光雷达的原始数据，即UDP数据包或者分组数据。
其中，压缩激光雷达的原始数据的算法多是利用雷达的工作原理和数据格式，将分组数据排列成2D矩阵的形式，再借用图像压缩方法进行压缩。但是，矩阵化的分组数据和图像的像素数据还是会有很不一样的分布，因此，图像的编解码技术中的很多算法并不适用于矩阵化的分组数据。
基于此，本申请实施例提供了一种雷达数据的压缩方法，以实现激光雷达数据的高效的压缩。
图1为本申请实施例的编解码系统10的示例性框图。编解码系统10中的压缩器12和解压缩器16代表可用于根据本申请实施例中描述的各种示例执行各技术的设备等。
如图1所示，编解码系统10包括编码端和解码端，编码端用于压缩雷达数据，将压缩后得到的码流提供给解码端，解码端对码流进行解压缩得到重建的雷达数据。
编码端包括压缩器12，另外即可选地，可包括数据源11和通信接口13。
数据源11可以包括或可以为任意类型的激光雷达，用于获取测量数据。数据源11也可以包括或可以为任意类型的内存或存储器。数据源11输出的数据为雷达的原始数据，该原始数据可以是UDP数据，也可以是经分组处理的分组数据。
压缩器12用于接收原始数据，对原始数据压缩后得到码流。
通信接口13可用于接收码流，并通过通信信道14向解码端发送该码流。
解码端包括解压缩器16，另外即可选地，可包括通信接口15和后处理器17。
通信接口15用于直接从编码端或从存储设备等任意其它设备接收码流，并将码流提供给解压缩器16。
通信接口13和通信接口15可用于通过编码端与解码端之间的直连通信链路，例如直接有线或无线连接等，或者通过任意类型的网络，例如有线网络、无线网络或其任意组合、任意类型的私网和公网或其任意类型的组合，发送或接收码流。
通信接口13和通信接口15均可配置为如图1中从编码端指向解码端的对应通信信道14的箭头所指示的单向通信接口，或双向通信接口，并且可用于发送和接收消息等，以建立连接，确认并交换与通信链路和/或例如编码后的图像数据传输等数据传输相关的任何其它信息，等等。
后处理器17用于对解码后的数据(也称为重建后的雷达数据)进行后处理，得到后处理雷达数据。后处理器17执行的后处理可以包括例如UDP数据/分组数据到点云的转换。
尽管图1示出了编码端和解码端作为独立的设备，但设备实施例也可以同时包括编码设备和解码设备或同时包括编码和解码的功能，即同时包括编码端或对应功能和解码端或对应功能。在这些实施例中，编码端或对应功能和解码端或对应功能可以使用相同硬件和/或软件或通过单独的硬件和/或软件或其任意组合来实现。
根据描述，图1所示的编码端和/或解码端中的不同单元或功能的存在和(准确)划分可能根据实际设备和应用而有所不同，这对技术人员来说是显而易见的。
编码端和解码端可包括各种设备中的任一种，包括任意类型的手持设备或固定设备，例如，笔记本电脑或膝上型电脑、智能手机、平板或平板电脑、台式计算机，等等，并可以不使用或使用任意类型的操作系统。在一些情况下，编码端和解码端可配备用于无线通信的组件。因此，编码端和解码端可以是无线通信设备。
需要说明的是，图1所示的编解码系统10仅仅是示例性的，在一些情况下，编码端和解码端可以应用于同一设备或不同设备，本申请实施例对雷达数据的编解码系统不做具体限定。
基于此，图2为本申请实施例的雷达数据的压缩方法的过程200的流程图。过程200可由编解码系统10中的编码端(尤其是压缩器12)执行。过程200描述为一系列的步骤或操作，应当理解的是，过程200可以以各种顺序执行和/或同时发生，不限于图2所示的执行顺序。假设激光雷达N次发射所采集到的测量数据正在使用编解码系统10，执行包括如下步骤的过程200来对上述测量数据进行压缩。过程200可以包括：
步骤201、获取待压缩数据。
如上所述，激光雷达的原始数据是指用户数据包协议(user datagram protocol，UDP)数据包或分组数据。图3a为本申请实施例的UDP数据的格式示意图，如图3a所示，UDP 数据包括附加信息和测量数据，其中，附加信息包括全球定位系统(global positioning system，GPS)时间戳、回波和协调世界时(universal time coordinated，UTC)，用于获取时间信息；测量数据包括10个数据块(block)，一个block对应激光雷达的一次发射，每次发射可以得到1个转动角(azimuth angle)测量数据和40个(表示40线激光雷达有40个镭射发射模组)频道单元(channel unit)测量数据。图3b为本申请实施例的分组数据的格式示意图，如图3b所示，对图3a所示的UDP数据进行整理得到分组数据，分组数据中仍然是一个block对应激光雷达的一次发射，一个block包括1个转动角(azimuth angle)测量数据和40组距离(distance)测量数据和信号强度(intensity)测量数据，可见距离测量数据和信号强度测量数据之间是一一对应的。
基于此，本申请实施例中的待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1，上述示例中N＝10。有多少组距离测量数据和信号强度测量数据与激光雷达的线数相关联，本申请实施例对此不做具体限定。此外，本申请实施例中的待压缩数据可以是指上述UDP数据，也可以是指上述分组数据，但为了便于后续步骤处理，如果待压缩数据是UDP数据，可以现将UDP数据整理成分组数据，再执行后续步骤。
在得到分组数据后，编码端可以将待压缩数据包含的多类测量数据分别排列成二维矩阵形式。示例性的，图3c为本申请实施例的二维矩阵的示意图，如图3c所示，矩阵的一行对应激光雷达的一个激光(laser)，矩阵的一列对应激光雷达的一次发射。按照上述示例，激光雷达共有40线(激光)，用发射10次，如果将距离测量数据表示成矩阵，那么该矩阵为40×10的二维矩阵。矩阵中的每个元素对应一个距离测量数据，编码端在对距离测量数据进行压缩时，可以按照图3c所示箭头的方向依次从矩阵中提取元素。
需要说明的是，图3c所示的元素提取顺序只是一种示例，其并不对实现方法构成限定，本申请实施例对此不做具体限定。另外，图3c以距离测量数据为例说明了二维矩阵和测量数据之间的对应关系，待压缩数据中的信号强度测量数据也可以采用相同的方法表示成二维矩阵，并且分别以相同或不同的顺序从矩阵中提取元素。
步骤202、当待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对距离测量数据进行压缩以得到第一码流。
本申请实施例中，针对不同类型的测量数据可以采用不同的压缩方式，其中，针对距离测量数据，编码端可以采用的压缩方法包括：
在一种可能的实现方式中，当待压缩数据包括多个0值距离测量数据时，对多个0值距离测量数据进行游程编码以得到第一码流。
如上所述，待压缩数据可以包括多个距离测量数据，将该多个距离测量数据排列成二位矩阵的形式后，按照矩阵元素的提取顺序，可能存在连续的、多个0值的距离测量数据，此时可以考虑对这些0值距离测量数据进行游程编码。例如，有连续10个0值距离测量数据，那么可以将其压缩表示成“0,10”，0表示距离测量数据的值为0，10表示有10个0值距离测量数据；或者，可以将其压缩表示成“0,(0,9),10”，0表示距离测量数据的值为0，(0,9)表示这一组0值距离测量数据中的首个0值的位置，10表示有10个0值距离测量数据；或者，可以将其压缩表示成“0,(0,9),(9,9)”，0表示距离测量数据的值为0，(0,9)表示这一组0值距离测量数据中的首个0值的位置，(9,9)表示这一组0值距离测量数据中的最末0值的位置。可见，采用游程编码可以大大减少压缩后的数据量，连续的0值距离测 量数据越多，压缩效率越高。需要说明的是，还可以采用其它的游程编码方式对多个0值距离测量数据进行压缩，本申请实施例对此不做具体限定。
在一种可能的实现方式中，当待压缩数据包括多个非0的距离测量数据时，对多个非0的距离测量数据进行基于残差的熵编码以得到第一码流。
当距离测量数据为非0值时，可以按照图3c所示的元素提取顺序，依次提取非0值距离测量数据，计算相邻两个非0值距离测量数据的残差，然后基于残差进行熵编码。需要说明的是，本申请实施例中对多个非0的距离测量数据进行基于残差的熵编码，还可以采用其它的实现方式计算，例如，基于之前的多个非0值距离测量数据计算当前非0值距离测量数据的残差，本申请实施例对此不做具体限定。
步骤203、根据距离测量数据对信号强度测量数据进行预测以得到第二码流。
同一组内的距离测量数据和信号强度测量数据之间具备对应关系，相应的，同一组内的距离测量数据和信号强度测量数据的取值也具备关联性，例如，距离测量数据为0时，通常与其对应的信号强度测量数据也为0，这是因为距离测量数据为0时，表示信号很弱不足以得到有效信息，因此忽略该距离测量数据，将其标记为0，相应的其所对应的信号强度测量数据也记为0。因此可以考虑将二者进行跨模态压缩，以达到最优的压缩效率。
在一种可能的实现方式中，当距离测量数据为0时，其所对应的信号强度测量数据也为0，因此针对这一类的信号强度测量数据，编码端可以不对其进行处理，即，编码端对0值距离测量数据对应的信号强度测量数据放弃处理，码流中将不给这样的信号强度测量数据保留位置。这样相当于编码端在压缩待压缩数据时，0值距离测量数据对应的信号强度测量数据可以视为不存在，可以大大减小数据量，0值越多压缩效率越高，进而节省了占用的码流。
在一种可能的实现方式中，同一组内的距离测量数据和信号强度测量数据的取值，除了上述0值对应0值的关系外，还可能在二者之间具备计算关系，例如，y＝a×x+b，y表示信号强度测量数据，x表示与y对应的距离测量数据，a和b可以时预先设定的参数；又例如，y＝a 1×x 1+a 2×x 2+b，y表示信号强度测量数据，x 1表示与y对应的距离测量数据，x 2表示x 1之前的距离测量数据，a 1、a 2和b可以时预先设定的参数。需要说明的是，同一组内的距离测量数据和信号强度测量数据之间的计算关系还可以采用其它方式表示，本申请实施例对此不做具体限定。
此时也可以参照上述方法，即，编码端对与距离测量数据具备计算关系的信号强度测量数据放弃处理，码流中将不给这样的信号强度测量数据保留位置。这样相当于编码端在压缩待压缩数据时，与距离测量数据具备计算关系的信号强度测量数据可以视为不存在，可以大大减小数据量，与距离测量数据具备计算关系的信号强度测量数据越多压缩效率越高，进而节省了占用的码流。
在一种可能的实现方式中，当距离测量数据为非0时，根据一个或多个非0的距离测量数据对与距离测量数据对应的信号强度测量数据进行预测以得到信号强度测量数据的残差，然后根据信号强度测量数据的残差获取第二码流。
可选的，可以根据同一组内的距离测量数据对信号强度测量数据进行预测，例如计算同一组内的距离测量数据和信号强度测量数据的残差，将该残差作为信号强度测量数据的残差进行熵编码。
可选的，可以根据同一组内的距离测量数据、以及该距离测量数据之前的距离测量数据共同对信号强度测量数据进行预测，例如根据前述多个距离测量数据计算中间值，再计算该中间值和信号强度测量数据的残差，将该残差作为信号强度测量数据的残差进行熵编码。
需要说明的是，本申请实施例可以采用多种方法得到信号强度测量数据的残差，对此不做具体限定。
在一种可能的实现方式中，当待压缩数据包括多个非0的距离测量数据时，对与多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到第二码流。
按照上述理论，当距离测量数据为0时，其所对应的信号强度测量数据也为0，反之，当距离测量数据为非0时，其所对应的信号强度测量数据也为非0。因此，编码端可以借鉴对非0的距离测量数据的压缩方式对非0的信号强度测量数据进行压缩，即
当信号强度测量数据为非0值时，可以按照图3c所示的元素提取顺序，依次提取非0值信号强度测量数据，计算相邻两个非0值信号强度测量数据的残差，然后基于残差进行熵编码。需要说明的是，本申请实施例中对多个非0的信号强度测量数据进行基于残差的熵编码，还可以采用其它的实现方式计算，例如，基于之前的多个非0值信号强度测量数据计算当前非0值信号强度测量数据的残差，本申请实施例对此不做具体限定。
在一种可能的实现方式中，获取非0的距离测量数据，根据一个或多个非0的距离测量数据对与非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差，根据非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差，最后根据第一残差和第二残差获取第二码流。
本申请实施例是对上述两种方式的结合，即多个非0的信号强度测量数据中，一部分非0的信号强度测量数据可以基于一个或多个非0的距离测量数据预测得到其残差值(第一残差)，另一部分非0的信号强度测量数据可以基于已压缩的、排在前面的非0的信号强度测量数据进行预测得到其残差值(第二残差)，最后对上述第一残差和第二残差进行熵编码。例如，排在首位的非0的信号强度测量数据可以基于与其对应的非0的距离测量数据预测得到其残差值，排在非首位的非0的信号强度测量数据可以基于排在其前面的非0的信号强度测量数据预测得到其残差值。
需要说明的是，同一组内的距离测量数据和信号强度测量数据的取值具备关联性，因此基于这种关联性，本申请实施例还可以采用其它方式跨模态的压缩距离测量数据和信号强度测量数据，对此不做具体限定。
此外，本申请实施例中，编码端还可以对待压缩数据中的时间测量数据和转动角车测量数据进行压缩：
当待压缩数据还包括时间测量数据时，对时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，
当待压缩数据还包括转动角测量数据时，对转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
游程编码、熵编码或者基于残差的熵编码的具体实现方式均可以参照上述距离测量数据的编码方式，此处不再赘述。
本申请实施例，通过基于同一组内的距离测量数据和信号强度测量数据之间的关联性，采用跨模态的方式对二者进行压缩，使得待压缩的数据量大大减小，尤其是0值越多压缩效率越高，进而节省了占用的码流。
图4为本申请实施例的雷达数据的解压缩方法的过程400的流程图。过程400可由编解码系统10中的解码端(尤其是解压缩器16)执行。过程400描述为一系列的步骤或操作，应当理解的是，过程400可以以各种顺序执行和/或同时发生，不限于图4所示的执行顺序。假设码流正在使用编解码系统10，执行包括如下步骤的过程400来对上述码流进行解压缩。过程400可以包括：
步骤401、获取码流。
码流包括第一码流、第二码流第三码流和第四码流中的至少一个，对于本申请是市里的方案，主要考虑距离测量数据和信号强度测量数据，因此码流中需要至少包括第一码流。
步骤402、根据码流获取与距离测量数据对应的第一码流。
可选的，码流中可以携带一些语法元素，用于指示码流中的不同字段的含义。例如，在第一码流的开始位置打上标签，以提示解码端此处开始之后的码流均是与距离测量数据对应的第一码流；又例如，用语法元素指示第一码流的起始位置(字节或比特位等)。
可选的，编码端和解码端可以预先约定不同的码流的长度，例如，与距离测量数据对应的第一码流、与信号强度测量数据对应的第二码流、与时间测量数据对应的第三码流、与转动角测量数据对应的第四码流，预先设置这四种码流的长度相同，并且顺序已定，那么解码端可以根据当前解析到的码流的字节或比特位确定是前述四个中的哪个码流。
需要说明的是，本申请实施例还可以采用其它方法获取第一码流，对此不做具体限定。
步骤403、对第一码流进行解压缩以得到距离测量数据。
在一种可能的实现方式中，对第一码流进行游程解码以得到多个连续的0值距离测量数据。与编码端相对应，解码端可以根据语法元素的指示，对码流中的相应字段采用游程解码，从而得到多个连续的0值距离测量数据。
在一种可能的实现方式中，对第一码流进行熵解码以得到除多个连续的0值距离测量数据之外的距离测量数据。
同样的，与编码端相对应，解码端可以根据语法元素的指示，对码流中的相应字段采用熵解码得到距离测量数据的残差，再采用与编码端相反的计算方式得到对应的距离测量数据，例如，将前述残差与上一个得到的距离测量数据相加即为当前距离测量数据，又例如，根据已得到的多个距离测量数据计算中间值，将前述残差与中间值相加即为当前距离测量数据。
步骤404、根据距离测量数据获取信号强度测量数据。
在一种可能的实现方式中，当距离测量数据为0时，将与距离测量数据对应的信号强度测量数据置为0。
与步骤203中的第一种实现方式相对应，当距离测量数据为0时，其所对应的信号强度测量数据也为0，因此解码端针对解压缩后的0值距离测量数据，可以直接将与其对应的信号强度测量数据置为0，而不需要从码流中解压缩得到，这样可以节省占用的码流，大大减小数据量，0值越多压缩效率越高。
在一种可能的实现方式中，当距离测量数据为非0时，仅根据距离测量数据计算得到 与距离测量数据对应的信号强度测量数据；或者，当距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与距离测量数据对应的信号强度测量数据。
与步骤203中的第二种实现方式相对应，同一组内的距离测量数据和信号强度测量数据的取值，除了上述0值对应0值的关系外，还可能在二者之间具备计算关系，因此解码端可以在得到非0值距离测量数据后，根据相应的计算关系计算得到与其对应的信号强度测量数据，例如，y＝a×x+b，y表示信号强度测量数据，x表示与y对应的距离测量数据，a和b可以时预先设定的参数；又例如，y＝a 1×x 1+a 2×x 2+b，y表示信号强度测量数据，x 1表示与y对应的距离测量数据，x 2表示x 1之前的距离测量数据，a 1、a 2和b可以时预先设定的参数。需要说明的是，同一组内的距离测量数据和信号强度测量数据之间的计算关系还可以采用其它方式表示，本申请实施例对此不做具体限定。这样同样可以节省占用的码流，大大减小数据量。
在一种可能的实现方式中，根据码流获取与信号强度测量数据对应的第二码流，对第二码流进行解压缩得到非0的信号强度测量数据的残差，根据已解码得到的一个或多个非0的距离测量数据和残差得到非0的信号强度测量数据。
与步骤203中的第三种实现方式相对应，可以根据同一组内的距离测量数据对信号强度测量数据进行预测，也可以根据同一组内的距离测量数据、以及该距离测量数据之前的距离测量数据共同对信号强度测量数据进行预测，基于此解码端可以通过解析码流得到信号强度测量数据的残差，再根据预测的方式通过残差和相应的距离测量数据得到信号强度测量数据。
在一种可能的实现方式中，根据码流获取与信号强度测量数据对应的第二码流，对第二码流进行解压缩以得到非0的信号强度测量数据。
与步骤203中的第四种实现方式相对应，解码端可以借鉴对非0的距离测量数据的解压缩方式对非0的信号强度测量数据进行解压缩。
在一种可能的实现方式中，根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据，根据非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
与步骤203中的第五种实现方式相对应，本申请实施例是对上述两种方式的结合，即多个非0的信号强度测量数据中，一部分非0的信号强度测量数据可以基于一个或多个非0的距离测量数据预测得到其残差值(第一残差)，另一部分非0的信号强度测量数据可以基于已压缩的、排在前面的非0的信号强度测量数据进行预测得到其残差值(第二残差)，因此解码端得到上述两种残差后，可以根据预测的方式通过残差和相应的距离测量数据得到信号强度测量数据。
此外，解码端还可以根据码流获取与时间测量数据对应的第三码流，对第三码流进行解压缩以得到时间测量数据。和/或，还可以根据码流获取与转动角测量数据对应的第四码流，对第四码流进行解压缩以得到转动角测量数据。解码端的处理与编码端相对应，此处不再赘述。
本申请实施例，通过基于同一组内的距离测量数据和信号强度测量数据之间的关联性，采用跨模态的方式对二者进行解压缩，由于码流中针对部分信号强度测量数据可以节省其 比特流，因此大大减小了码流长度，提高压缩效率。
此外，本申请实施例还提供了一种雷达数据的压缩/解压缩方法，即，编码端获取待压缩数据，待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1。当待压缩数据包括多个0值距离测量数据时，对多个0值距离测量数据进行游程编码以得到第一码流。解码端获取码流，根据码流获取与距离测量数据对应的第一码流；对第一码流进行游程解码以得到多个连续的0值距离测量数据。
针对待压缩数据包括多个0值距离测量数据时，可以不多其采用熵编码的方式，而是游程编码，这样可以大大减少数据量，提高压缩效率。相应的，解码端可以根据码流中的语法元素的指示，对其中的部分码流采用游程解码的方式得到距离测量数据，同样可以提高解压缩的效率。
此时，针对非0的距离测量数据、信号强度测量数据、时间测量数据和转动角测量数据分别进行游程编码、熵编码或者基于残差的熵编码中的任意一种压缩处理，此处不再赘述。
示例性的，图5为本申请实施例的雷达数据的压缩/解压缩方法的适宜性流程图，如图5所示，激光雷达测量得到的UDP数据经整理得到分组数据(packet data)，分组数据包括时间测量数据、转动角测量数据、距离测量数据和信号强度测量数据(intensity)，编码端的压缩器可以包括多个编码模块，时间编码模块对时间测量数据进行压缩处理，转动角编码模块对转动角测量数据进行压缩处理，距离编码模块和信号强度编码模块依次对时间测量数据和信号强度测量数据进行压缩处理。压缩后得到的数据传输给解码端。
解码端的解压缩器也包括多个解码模块，时间解码模块对时间测量数据对应的码流进行解压缩处理得到时间测量数据，转动角解码模块对转动角测量数据对应的码流进行解压缩处理得到转动角测量数据，距离解码模块和信号强度解码模块依次对距离测量数据对应的码流和信号强度测量数据对应的码流进行解压缩处理得到距离测量数据和信号强度测量数据。最后将时间测量数据、转动角测量数据距离测量数据和信号强度测量数据重建的分组数据转化为点云。
图6为本申请实施例压缩装置600的一个示例性的结构示意图，如图6所示，本实施例的装置600可以应用于压缩器12。该装置600可以包括：获取模块601和压缩模块602。其中，
获取模块601，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；压缩模块602，用于当所述待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对所述距离测量数据进行压缩以得到第一码流；根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流。
在一种可能的实现方式中，所述压缩模块602，具体用于当所述距离测量数据为非0时，根据一个或多个非0的距离测量数据对与所述距离测量数据对应的信号强度测量数据进行预测以得到所述信号强度测量数据的残差；根据所述信号强度测量数据的残差获取所述第二码流。
在一种可能的实现方式中，所述压缩模块602，具体用于当所述待压缩数据包括多个非0的距离测量数据时，对与所述多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到所述第二码流。
在一种可能的实现方式中，所述压缩模块602，具体用于获取非0的距离测量数据；根据一个或多个非0的距离测量数据对与所述非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差；根据所述非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差；根据所述第一残差和所述第二残差获取所述第二码流。
在一种可能的实现方式中，所述压缩模块602，具体用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到所述第一码流；或者，当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
在一种可能的实现方式中，所述压缩模块602，还用于当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
在一种可能的实现方式中，获取模块601，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；压缩模块602，用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到第一码流。
在一种可能的实现方式中，所述压缩模块602，还用于当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
在一种可能的实现方式中，所述压缩模块602，还用于当所述待压缩数据还包括信号强度测量数据时，对所述信号强度测量数据进行游程编码码、熵编码或者基于残差的熵编码以得到第二码流；和/或，当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
图7为本申请实施例解压缩装置700的一个示例性的结构示意图，如图7所示，本实施例的装置700可以应用于解压缩器16。该装置700可以包括：获取模块701和解压缩模块702。其中，
获取模块701，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；解压缩模块702，用于对所述第一码流进行解压缩以得到所述距离测量数据；根据所述距离测量数据获取信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块702，具体用于当所述距离测量数据为0时，将与所述距离测量数据对应的信号强度测量数据置为0。
在一种可能的实现方式中，所述解压缩模块702，具体用于当所述距离测量数据为非0时，仅根据所述距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据；或者，当所述距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块702，具体用于根据所述码流获取与所述 信号强度测量数据对应的第二码流；对所述第二码流进行解压缩得到非0的信号强度测量数据的残差；根据已解码得到的一个或多个非0的距离测量数据和所述残差得到所述非0的信号强度测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与所述信号强度测量数据对应的第二码流；所述解压缩模块702，还用于对所述第二码流进行解压缩以得到非0的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块702，具体用于根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据；根据所述非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
在一种可能的实现方式中，所述解压缩模块702，具体用于对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
在一种可能的实现方式中，所述解压缩模块702，具体用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与时间测量数据对应的第三码流；所述解压缩模块702，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与转动角测量数据对应的第四码流；所述解压缩模块702，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
在一种可能的实现方式中，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
在一种可能的实现方式中，获取模块701，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；解压缩模块702，用于对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
在一种可能的实现方式中，所述解压缩模块702，还用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与信号强度测量数据对应的第二码流；所述解压缩模块702，还用于对所述第二码流进行解压缩以得到所述信号强度测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与时间测量数据对应的第三码流；所述解压缩模块702，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
在一种可能的实现方式中，所述获取模块701，还用于根据所述码流获取与转动角测量数据对应的第四码流；所述解压缩模块702，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
在一种可能的实现方式中，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
在实现过程中，上述方法实施例的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。处理器可以是通用处理器、数字信号处理器(digital signal processor,DSP)、特定应用集成电路(application-specific integrated circuit，ASIC)、现场可编程门阵列(field programmable gate array,FPGA)或其他可编程逻辑器件、分立门或者晶体管逻辑 器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。本申请实施例公开的方法的步骤可以直接体现为硬件编码处理器执行完成，或者用编码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。
上述各实施例中提及的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(dynamic RAM，DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(direct rambus RAM，DR RAM)。应注意，本文描述的系统和方法的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机 软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。

Claims (55)

  1. 一种雷达数据的压缩方法，其特征在于，包括：
    获取码流；
    根据所述码流获取与距离测量数据对应的第一码流；
    对所述第一码流进行解压缩以得到所述距离测量数据；
    根据所述距离测量数据获取信号强度测量数据。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述距离测量数据获取信号强度测量数据，包括：
    当所述距离测量数据为0时，将与所述距离测量数据对应的信号强度测量数据置为0。
  3. 根据权利要求1或2所述的方法，其特征在于，所述根据所述距离测量数据获取信号强度测量数据，包括：
    当所述距离测量数据为非0时，仅根据所述距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据；或者，
    当所述距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据。
  4. 根据权利要求1或2所述的方法，其特征在于，所述根据所述距离测量数据获取信号强度测量数据，包括：
    根据所述码流获取与所述信号强度测量数据对应的第二码流；
    对所述第二码流进行解压缩得到非0的信号强度测量数据的残差；
    根据已解码得到的一个或多个非0的距离测量数据和所述残差得到所述非0的信号强度测量数据。
  5. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与所述信号强度测量数据对应的第二码流；
    对所述第二码流进行解压缩以得到非0的信号强度测量数据。
  6. 根据权利要求1或2所述的方法，其特征在于，所述根据所述距离测量数据获取信号强度测量数据，包括：
    根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据；
    根据所述非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
  7. 根据权利要求1-6中任一项的方法，其特征在于，所述对所述第一码流进行解压缩以得到所述距离测量数据，包括：
    对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
  8. 根据权利要求7所述的方法，其特征在于，所述对所述第一码流进行解压缩以得到所述距离测量数据，包括：
    对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
  9. 根据权利要求1-8中任一项所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与时间测量数据对应的第三码流；
    对所述第三码流进行解压缩以得到所述时间测量数据。
  10. 根据权利要求1-9中任一项所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与转动角测量数据对应的第四码流；
    对所述第四码流进行解压缩以得到所述转动角测量数据。
  11. 根据权利要求1-10中任一项所述的方法，其特征在于，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
  12. 一种雷达数据的压缩方法，其特征在于，包括：
    获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；
    当所述待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对所述距离测量数据进行压缩以得到第一码流；
    根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流。
  13. 根据权利要求12所述的方法，其特征在于，所述根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流，包括：
    当所述距离测量数据为非0时，根据一个或多个非0的距离测量数据对与所述距离测量数据对应的信号强度测量数据进行预测以得到所述信号强度测量数据的残差；
    根据所述信号强度测量数据的残差获取所述第二码流。
  14. 根据权利要求12所述的方法，其特征在于，所述根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流，包括：
    当所述待压缩数据包括多个非0的距离测量数据时，对与所述多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到所述第二码流。
  15. 根据权利要求12所述的方法，其特征在于，所述根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流，包括：
    获取非0的距离测量数据；
    根据一个或多个非0的距离测量数据对与所述非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差；
    根据所述非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差；
    根据所述第一残差和所述第二残差获取所述第二码流。
  16. 根据权利要求12-15中任一项所述的方法，其特征在于，所述对所述距离测量数据进行压缩以得到第一码流，包括：
    当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到所述第一码流；或者，
    当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
  17. 根据权利要求12-16中任一项所述的方法，其特征在于，所述方法还包括：
    当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，
    当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
  18. 一种雷达数据的压缩方法，其特征在于，包括：
    获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；
    当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到第一码流。
  19. 根据权利要求18所述的方法，其特征在于，所述方法还包括：
    当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
  20. 根据权利要求18或19所述的方法，其特征在于，所述方法还包括：
    当所述待压缩数据还包括信号强度测量数据时，对所述信号强度测量数据进行游程编码码、熵编码或者基于残差的熵编码以得到第二码流；和/或，
    当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，
    当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
  21. 一种雷达数据的解压缩方法，其特征在于，包括：
    获取码流；
    根据所述码流获取与距离测量数据对应的第一码流；
    对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
  22. 根据权利要求21所述的方法，其特征在于，所述方法还包括：
    对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
  23. 根据权利要求21或22所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与信号强度测量数据对应的第二码流；
    对所述第二码流进行解压缩以得到所述信号强度测量数据。
  24. 根据权利要求21-23中任一项所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与时间测量数据对应的第三码流；
    对所述第三码流进行解压缩以得到所述时间测量数据。
  25. 根据权利要求21-24中任一项所述的方法，其特征在于，所述方法还包括：
    根据所述码流获取与转动角测量数据对应的第四码流；
    对所述第四码流进行解压缩以得到所述转动角测量数据。
  26. 根据权利要求23-25中任一项所述的方法，其特征在于，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
  27. 一种解压缩装置，其特征在于，包括：
    获取模块，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；
    解压缩模块，用于对所述第一码流进行解压缩以得到所述距离测量数据；根据所述距离测量数据获取信号强度测量数据。
  28. 根据权利要求27所述的装置，其特征在于，所述解压缩模块，具体用于当所述距离测量数据为0时，将与所述距离测量数据对应的信号强度测量数据置为0。
  29. 根据权利要求27或28所述的装置，其特征在于，所述解压缩模块，具体用于当 所述距离测量数据为非0时，仅根据所述距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据；或者，当所述距离测量数据为非0时，仅根据已解码得到的一个或多个非0的距离测量数据计算得到与所述距离测量数据对应的信号强度测量数据。
  30. 根据权利要求27或28所述的装置，其特征在于，所述解压缩模块，具体用于根据所述码流获取与所述信号强度测量数据对应的第二码流；对所述第二码流进行解压缩得到非0的信号强度测量数据的残差；根据已解码得到的一个或多个非0的距离测量数据和所述残差得到所述非0的信号强度测量数据。
  31. 根据权利要求28所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与所述信号强度测量数据对应的第二码流；
    所述解压缩模块，还用于对所述第二码流进行解压缩以得到非0的信号强度测量数据。
  32. 根据权利要求27或28所述的装置，其特征在于，所述解压缩模块，具体用于根据已解码得到的一个或多个非0的距离测量数据预测得到非0的信号强度测量数据；根据所述非0的信号强度测量数据预测得到另一个非0的信号强度测量数据。
  33. 根据权利要求27-32中任一项的装置，其特征在于，所述解压缩模块，具体用于对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
  34. 根据权利要求33所述的装置，其特征在于，所述解压缩模块，具体用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
  35. 根据权利要求27-34中任一项所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与时间测量数据对应的第三码流；
    所述解压缩模块，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
  36. 根据权利要求27-35中任一项所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与转动角测量数据对应的第四码流；
    所述解压缩模块，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
  37. 根据权利要求27-36中任一项所述的装置，其特征在于，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
  38. 一种压缩装置，其特征在于，包括：
    获取模块，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；
    压缩模块，用于当所述待压缩数据包括相对应的距离测量数据和信号强度测量数据时，对所述距离测量数据进行压缩以得到第一码流；根据所述距离测量数据对所述信号强度测量数据进行预测以得到第二码流。
  39. 根据权利要求38所述的装置，其特征在于，所述压缩模块，具体用于当所述距离测量数据为非0时，根据一个或多个非0的距离测量数据对与所述距离测量数据对应的信号强度测量数据进行预测以得到所述信号强度测量数据的残差；根据所述信号强度测量数据的残差获取所述第二码流。
  40. 根据权利要求38所述的装置，其特征在于，所述压缩模块，具体用于当所述待压缩数据包括多个非0的距离测量数据时，对与所述多个非0的距离测量数据对应的多个信号强度测量数据进行基于残差的熵编码以得到所述第二码流。
  41. 根据权利要求38所述的装置，其特征在于，所述压缩模块，具体用于获取非0的 距离测量数据；根据一个或多个非0的距离测量数据对与所述非0的距离测量数据对应的非0的信号强度测量数据进行预测得到第一残差；根据所述非0的信号强度测量数据对另一个非0的信号强度测量数据进行预测以得到第二残差；根据所述第一残差和所述第二残差获取所述第二码流。
  42. 根据权利要求38-41中任一项所述的装置，其特征在于，所述压缩模块，具体用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到所述第一码流；或者，当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
  43. 根据权利要求38-42中任一项所述的装置，其特征在于，所述压缩模块，还用于当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
  44. 一种压缩装置，其特征在于，包括：
    获取模块，用于获取待压缩数据，所述待压缩数据是激光雷达N次发射所采集到的测量数据，N≥1；
    压缩模块，用于当所述待压缩数据包括多个0值距离测量数据时，对所述多个0值距离测量数据进行游程编码以得到第一码流。
  45. 根据权利要求44所述的装置，其特征在于，所述压缩模块，还用于当所述待压缩数据包括多个非0的距离测量数据时，对所述多个非0的距离测量数据进行基于残差的熵编码以得到所述第一码流。
  46. 根据权利要求44或45所述的装置，其特征在于，所述压缩模块，还用于当所述待压缩数据还包括信号强度测量数据时，对所述信号强度测量数据进行游程编码码、熵编码或者基于残差的熵编码以得到第二码流；和/或，当所述待压缩数据还包括时间测量数据时，对所述时间测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第三码流；和/或，当所述待压缩数据还包括转动角测量数据时，对所述转动角测量数据进行游程编码、熵编码或者基于残差的熵编码以得到第四码流。
  47. 一种解压缩装置，其特征在于，包括：
    获取模块，用于获取码流；根据所述码流获取与距离测量数据对应的第一码流；
    解压缩模块，用于对所述第一码流进行游程解码以得到多个连续的0值距离测量数据。
  48. 根据权利要求47所述的装置，其特征在于，所述解压缩模块，还用于对所述第一码流进行熵解码以得到除所述多个连续的0值距离测量数据之外的距离测量数据。
  49. 根据权利要求47或48所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与信号强度测量数据对应的第二码流；
    所述解压缩模块，还用于对所述第二码流进行解压缩以得到所述信号强度测量数据。
  50. 根据权利要求47-49中任一项所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与时间测量数据对应的第三码流；
    所述解压缩模块，还用于对所述第三码流进行解压缩以得到所述时间测量数据。
  51. 根据权利要求47-50中任一项所述的装置，其特征在于，所述获取模块，还用于根据所述码流获取与转动角测量数据对应的第四码流；
    所述解压缩模块，还用于对所述第四码流进行解压缩以得到所述转动角测量数据。
  52. 根据权利要求48-51中任一项所述的装置，其特征在于，所述解压缩包括游程解码、熵解码或者基于残差的熵解码。
  53. 一种电子设备，其特征在于，包括：
    一个或多个处理器；
    存储器，用于存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-26中任一项所述的方法。
  54. 一种计算机可读存储介质，其特征在于，包括计算机程序，所述计算机程序在计算机上被执行时，使得所述计算机执行权利要求1-26中任一项所述的方法。
  55. 一种计算机程序产品，其特征在于，所述计算机程序产品包括计算机程序代码，当所述计算机程序代码在计算机上运行时，用于执行权利要求1-26中任一项所述的方法。
PCT/CN2022/132107 2021-11-23 2022-11-16 雷达数据的压缩方法和装置 Ceased WO2023093582A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP22897665.0A EP4407959A4 (en) 2021-11-23 2022-11-16 METHOD AND APPARATUS FOR COMPRESSION OF RADAR DATA
US18/672,686 US20240314350A1 (en) 2021-11-23 2024-05-23 Method for compressing lidar data and apparatus

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111393470.5A CN114244907B (zh) 2021-11-23 2021-11-23 雷达数据的压缩方法和装置
CN202111393470.5 2021-11-23

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/672,686 Continuation US20240314350A1 (en) 2021-11-23 2024-05-23 Method for compressing lidar data and apparatus

Publications (1)

Publication Number Publication Date
WO2023093582A1 true WO2023093582A1 (zh) 2023-06-01

Family

ID=80750550

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/132107 Ceased WO2023093582A1 (zh) 2021-11-23 2022-11-16 雷达数据的压缩方法和装置

Country Status (4)

Country Link
US (1) US20240314350A1 (zh)
EP (1) EP4407959A4 (zh)
CN (1) CN114244907B (zh)
WO (1) WO2023093582A1 (zh)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114244907B (zh) * 2021-11-23 2024-01-16 华为技术有限公司 雷达数据的压缩方法和装置
CN117997349A (zh) * 2022-11-02 2024-05-07 华为技术有限公司 一种雷达数据的编码、解码方法、编码装置及解码装置
CN116455398B (zh) * 2023-04-19 2026-04-14 广东松山职业技术学院 一种激光雷达的数据压缩还原传输方法、系统和存储介质
CN119001760A (zh) * 2023-05-22 2024-11-22 安徽寒武纪信息科技有限公司 数据压缩方法、装置、设备及介质
CN120881271A (zh) * 2024-04-30 2025-10-31 华为技术有限公司 编解码方法、装置、码流及计算机可读存储介质
CN120881290A (zh) * 2024-04-30 2025-10-31 华为技术有限公司 编解码方法、装置、码流及计算机可读存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8306747B1 (en) * 2007-01-19 2012-11-06 Starodub, Inc. Travel way measurement system
US20200284883A1 (en) * 2019-03-08 2020-09-10 Osram Gmbh Component for a lidar sensor system, lidar sensor system, lidar sensor device, method for a lidar sensor system and method for a lidar sensor device
CN111736114A (zh) * 2020-08-21 2020-10-02 武汉煜炜光学科技有限公司 一种提高激光雷达数据传输速度的方法和激光雷达
CN111882623A (zh) * 2020-07-15 2020-11-03 富德康(北京)科技股份有限公司 一种三维空间测量结果数据的压缩方法
CN114244907A (zh) * 2021-11-23 2022-03-25 华为技术有限公司 雷达数据的压缩方法和装置

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0817052B2 (ja) * 1991-07-02 1996-02-21 住友ベークライト株式会社 導電性樹脂ペースト
JP2002012915A (ja) * 2000-06-28 2002-01-15 Toyota Motor Corp 倣い制御が容易な高周波加熱用ヘッド
CN200941116Y (zh) * 2006-07-10 2007-08-29 武汉大学 一种对激光雷达信号动态范围压缩的装置
CN102062855A (zh) * 2010-11-03 2011-05-18 安徽四创电子股份有限公司 基于游程差值编码的雷达回波压缩/解压算法
US9274221B2 (en) * 2013-02-01 2016-03-01 GM Global Technology Operations LLC Method and apparatus for remote object sensing employing compressive sensing
JP6671968B2 (ja) * 2016-01-13 2020-03-25 株式会社東芝 信号処理装置、レーダ受信機、信号処理方法及びプログラム
EP3418770B1 (en) * 2016-03-09 2020-08-05 Mitsubishi Electric Corporation Synthetic-aperture-radar signal processing device
CN106908790B (zh) * 2017-02-28 2019-08-06 西安电子科技大学 一种机载sar雷达速度的优化估计方法
EP3895431A1 (en) * 2018-12-14 2021-10-20 PCMS Holdings, Inc. System and method for procedurally colorizing spatial data
CN109857709A (zh) * 2019-02-22 2019-06-07 中国人民解放军空军研究院战略预警研究所 一种数据压缩方法及装置
CN113615207A (zh) * 2019-03-21 2021-11-05 Lg电子株式会社 点云数据发送装置、点云数据发送方法、点云数据接收装置和点云数据接收方法
US11461932B2 (en) * 2019-06-11 2022-10-04 Tencent America LLC Method and apparatus for point cloud compression
CN111142102B (zh) * 2019-12-26 2022-07-19 华为技术有限公司 一种呼吸数据计算方法以及相关设备
CN111175709B (zh) * 2019-12-27 2023-02-24 中国电子科技集团公司第十四研究所 一种基于误差抑制的面向大范围气象雷达的拼图方法
CN112782697B (zh) * 2020-12-24 2023-08-22 成都福瑞空天科技有限公司 一种无人机机载防撞雷达系统和工作方法
CN117997349A (zh) * 2022-11-02 2024-05-07 华为技术有限公司 一种雷达数据的编码、解码方法、编码装置及解码装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8306747B1 (en) * 2007-01-19 2012-11-06 Starodub, Inc. Travel way measurement system
US20200284883A1 (en) * 2019-03-08 2020-09-10 Osram Gmbh Component for a lidar sensor system, lidar sensor system, lidar sensor device, method for a lidar sensor system and method for a lidar sensor device
CN111882623A (zh) * 2020-07-15 2020-11-03 富德康(北京)科技股份有限公司 一种三维空间测量结果数据的压缩方法
CN111736114A (zh) * 2020-08-21 2020-10-02 武汉煜炜光学科技有限公司 一种提高激光雷达数据传输速度的方法和激光雷达
CN114244907A (zh) * 2021-11-23 2022-03-25 华为技术有限公司 雷达数据的压缩方法和装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4407959A4

Also Published As

Publication number Publication date
CN114244907A (zh) 2022-03-25
US20240314350A1 (en) 2024-09-19
EP4407959A1 (en) 2024-07-31
EP4407959A4 (en) 2025-03-19
CN114244907B (zh) 2024-01-16

Similar Documents

Publication Publication Date Title
CN114244907B (zh) 雷达数据的压缩方法和装置
JP2023129667A (ja) 三次元データ符号化方法、三次元データ復号方法、三次元データ符号化装置、及び三次元データ復号装置
EP3734973B1 (en) Method and encoder relating to encoding of pixel values to accomplish lossless compression of a digital image
US20240249441A1 (en) Method, apparatus and medium for point cloud coding
US20240242393A1 (en) Method, apparatus and medium for point cloud coding
JP2020501475A (ja) ロスレスデータ圧縮
CN103379333A (zh) 编解码方法、视频序列码流的编解码方法及其对应的装置
CN119923839A (zh) 数据压缩传输方法、装置、设备以及存储介质
US20240070924A1 (en) Compression of temporal data by using geometry-based point cloud compression
CN112187369B (zh) 传感器数据编码方法和装置、电子设备及存储介质
JP2025541972A (ja) レーダデータの符号化および復号方法、符号化装置、ならびに復号装置
CN107431809B (zh) 图像处理的方法和装置
CN111684804B (zh) 一种数据编码、数据解码方法、设备及存储介质
WO2024093196A1 (zh) 一种雷达数据的编码、解码方法、编码装置及解码装置
JP6765355B2 (ja) デコーダ、エンコーダおよび符号化値を復号化する方法
CN110784722A (zh) 编解码方法、编解码装置、编解码系统及存储介质
CN120881290A (zh) 编解码方法、装置、码流及计算机可读存储介质
CN120881293A (zh) 编解码方法、装置、码流及计算机可读存储介质
CN110545431A (zh) 视频解码方法及装置、视频编码方法及装置
US20250336098A1 (en) Method, apparatus, and medium for point cloud coding
CN118743219B (zh) 编解码方法、码流、编码器、解码器以及存储介质
US20260129226A1 (en) Encoding method, decoding method, encoding device, and decoding device
US12132503B1 (en) Lossless compression for multivariate selective encoding
CN120881271A (zh) 编解码方法、装置、码流及计算机可读存储介质
CN116980972A (zh) 数据传输方法及相关装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22897665

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2022897665

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2022897665

Country of ref document: EP

Effective date: 20240422

NENP Non-entry into the national phase

Ref country code: DE