CN116299536B - Laser radar SLAM degradation detection method and system based on pose constraint - Google Patents
Laser radar SLAM degradation detection method and system based on pose constraintInfo
- Publication number
- CN116299536B CN116299536B CN202310207645.1A CN202310207645A CN116299536B CN 116299536 B CN116299536 B CN 116299536B CN 202310207645 A CN202310207645 A CN 202310207645A CN 116299536 B CN116299536 B CN 116299536B
- Authority
- CN
- China
- Prior art keywords
- constraint
- pose
- degradation
- laser radar
- robot
- 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.)
- Active
Links
Classifications
-
- 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
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides a laser radar SLAM degradation detection method and system based on pose constraint, comprising the steps of collecting continuous frame point clouds through a laser radar sensor; analyzing the continuous frame point cloud to obtain a point cloud constraint relation of robot pose disturbance rejection robustness; and carrying out robot pose constraint disturbance recognition according to the point cloud constraint relation to obtain an SLAM degradation direction. The method can solve the problem that the laser radar SLAM is difficult to detect when losing efficacy in a degradation scene, provides early warning for the stable operation of the laser SLAM, can realize higher detection accuracy, and is beneficial to practical application.
Description
Technical Field
The invention relates to the field of computer vision, in particular to a laser radar SLAM degradation detection method and system based on pose constraint.
Background
The laser radar is a sensor with wide application at present, wherein a transmitting module transmits laser with specific wavelength in pulse, frequency modulation, amplitude modulation and other modes based on an optical principle, a receiving module detects the laser reflected after transmitting by a diode element, and accurate ranging can be obtained through light flight time. The laser radar is widely applied to applications such as target detection, scene modeling, robot navigation and the like, and has wider commercial and academic prospects.
The synchronous mapping and positioning algorithm (SLAM) is to use information provided by the sensor to build a map of the environment scene, and determine the corresponding position of the sensor in the map. At present, SLAM technology is widely applied to autonomous positioning and navigation tasks of robots. The SLAM algorithm based on the laser radar is widely applied to high-difficulty positioning navigation tasks such as unmanned logistics, unmanned minibus and the like due to high-precision ranging information and robust positioning results.
One of the application challenges of the lidar SLAM algorithm is the algorithm failure problem facing a specific scenario. The scenes comprise tunnels, galleries, outdoor open parking lots, highways and the like, and are characterized in that the scenes have fewer structural textures, scene degradation occurs, and the data constraint provided by the laser radar is insufficient during positioning so that an algorithm is disabled. The degradation problem can be processed by methods such as multi-sensor data fusion, state space modeling compensation and the like, and how to identify and detect the degradation scene of the laser radar SLAM becomes a key step of algorithm optimization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a laser radar SLAM degradation detection method and system based on pose constraint.
According to one aspect of the present invention, there is provided a laser radar SLAM degradation detection method based on pose constraint, including:
collecting continuous frame point clouds through a laser radar sensor;
Analyzing the continuous frame point cloud to obtain a point cloud constraint relation of robot pose disturbance rejection robustness;
and carrying out robot pose constraint disturbance recognition according to the point cloud constraint relation to obtain a degradation direction.
Preferably, the analyzing the continuous frame point cloud to obtain a point cloud constraint relationship of robot pose disturbance rejection robustness includes:
Approximating the local surface of the continuous frame point cloud as a plane, each measurement point satisfying the following relation:
Wherein, the Is a unit vector of a laser beam in a radar coordinate system, is used for indicating the direction of the beam,Corresponding to its beam length, i e {1,2,..m } is the index of the laser spot beam,Is a normal vector of the approximate plane, p i,0 is a point on the approximate plane,The position and attitude of the robot are described separately;
Will be And d i, obtaining a point cloud constraint relation of the robot pose disturbance rejection robustness as follows:
preferably, the performing robot pose constraint disturbance recognition according to the point cloud constraint relationship includes:
in the point cloud registration process, the position and posture (R, t) of the robot change less, and the change of (R, t) can be regarded as disturbance;
And judging whether the laser radar SLAM is subject to scene degradation or not by calculating the sensitivity degree of the laser measurement distance rho i to (R, t) disturbance.
Preferably, if the pose of the robot is disturbed and the laser measurement distance is changed greatly, the constraint of the pose of the robot is described to be strong currently, otherwise, if the pose of the robot is disturbed and the laser measurement distance is not changed greatly, the constraint of the robot is described to be weak currently and the system is in a degradation environment.
Preferably, the obtaining the degradation direction includes:
Based on the assumption of small angle transformation in robot pose, the derivative problem is linearized by using R.apprxeq.I+ [ θ ] ×:
Based on the hidden function theorem, an equation is established:
Derivation of cocoa the product can be obtained by the method,
According to the formula obtained by the hidden function theorem, an F matrix and a T matrix are constructed, wherein the sensitivity of laser constraint to translation parameters and the sensitivity of laser constraint to rotation parameters are respectively represented:
Performing eigenvalue decomposition on the F matrix and the T matrix to obtain a group of eigenvectors,
The feature vector corresponding to the minimum feature value is the current degradation direction, wherein the minimum feature value of the F matrix is used for judging the degradation direction of the translation quantity, and the minimum feature value of the T matrix is used for judging the degradation direction of the rotation.
Preferably, the projection of the matrix on any feature vector is the expansion and contraction of the feature vector, the expansion and contraction proportion is a feature value, the feature value reflects the proportion of the projection of the matrix in the direction of the feature vector, and the smaller the feature value is, the smaller the constraint of the matrix in the direction of the feature vector is, the smaller the constraint is, and the degradation is easy to occur in the direction.
Preferably, if λ min is smaller than the set threshold, it represents that degradation occurs in the current scene, and meanwhile, the feature vector corresponding to the minimum feature value λ min is the current degradation direction.
According to a second aspect of the present invention, there is provided a laser radar SLAM degradation detection system based on pose constraint, comprising:
the data module is used for collecting continuous frame point clouds through a laser radar sensor;
The constraint relation module analyzes the continuous frame point clouds and obtains a point cloud constraint relation of robot pose disturbance rejection robustness;
and the disturbance recognition module is used for carrying out robot pose constraint disturbance recognition according to the point cloud constraint relation.
According to a third aspect of the present invention, there is provided a terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being operable, when executing the program, to perform the pose constraint based lidar SLAM degradation detection method or to run the pose constraint based lidar SLAM degradation detection system described above.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, is operative to perform the above-described pose constraint-based laser radar SLAM degradation detection method, or to run the above-described pose constraint-based laser radar SLAM degradation detection system.
Compared with the prior art, the invention has the following beneficial effects:
the method can solve the problem that the laser radar SLAM is difficult to detect when losing efficacy in a degradation scene, provides early warning for the stable operation of the laser SLAM, can realize higher detection accuracy, and is beneficial to practical application.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a laser radar SLAM degradation detection method based on pose constraints in an embodiment of the invention;
FIG. 2 is a flow chart of laser radar SLAM degradation detection based on pose constraints in a preferred embodiment of the present invention;
FIG. 3 is a schematic representation of robot pose constraints in an embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
First, terms (SLAM and degraded scene) involved in the present invention are explained. SLAM is to build map and position algorithm synchronously, and to build map of the environment scene by using the information provided by the sensor and to determine the corresponding position of the sensor in the map. The SLAM technology is widely applied to environment modeling and autonomous positioning navigation tasks of robots. The laser radar SLAM is a map which draws a laser scanning scene according to the point cloud information acquired by the laser radar sensor, and determines the position of the laser radar sensor in the map. Degraded scenario-the structural textures in the laser radar usage scenario are less, so that the sensor can not provide enough constraint when the data provided in the scenario face the optimization problem of the system, and the optimization object falls into the locally optimal solution to cause the system failure. The degradation scene of the laser radar comprises tunnels, galleries, outdoor open parking lots, highways and the like.
The invention provides an embodiment, a laser radar SLAM degradation detection method based on pose constraint, which is shown in fig. 1 and comprises the following steps:
S100, collecting continuous frame point clouds through a laser radar sensor;
S200, analyzing the continuous frame point clouds of the S200 to obtain a point cloud constraint relation of the robot pose disturbance rejection robustness;
S300, carrying out robot pose constraint disturbance recognition according to the point cloud constraint relation of S200, and obtaining the degradation direction.
The embodiment solves the problem that the laser radar SLAM is difficult to detect in failure under a degradation scene, provides early warning for the stable operation of the laser radar SLAM, can realize higher detection accuracy, and is beneficial to practical application.
In a preferred embodiment of the present invention, S100 is implemented, where a laser radar sensor collects a continuous frame point cloud, specifically, a robot carries a laser radar, and the laser radar emits laser light with a specific wavelength by means of pulse, frequency modulation, amplitude modulation, and the like, and after the laser radar irradiates an object, the laser light is reflected, and the laser light reflected after the emission is received, so as to obtain original point cloud data. Each frame includes a cloud of points, including the point clouds emitted by the m laser points. Wherein the reflection of each laser light on the object corresponds to a measurement point.
In a preferred embodiment of the present invention, S200 is implemented, that is, for laser radar positioning, pose constraints of each measurement point of the laser radar on the robot are obtained, see fig. 2, and the specific procedure is as follows:
Approximating the local surface of the point cloud as a plane, then for each measurement point:
Wherein the method comprises the steps of Is a unit vector of a laser beam in a radar coordinate system, is used for indicating the direction of the beam,Corresponding to its beam length, i e {1,2,..m } is the index of the laser spot beam,Is a normal vector of the approximate plane, p i,0 is a point on the approximate plane,The position and pose of the robot are described separately, see fig. 3.
Will beLet d i be:
and taking the model as a point cloud constraint relation of robot pose disturbance rejection robustness. The constraint relation describes the constraint relation between the pose of the robot and the laser point, and provides an accurate model foundation for further detecting whether the laser point measurement is degraded or not.
In a preferred implementation of the present invention, S300 is implemented, where in point cloud registration (that is, as the robot moves, the lidar continuously obtains two frames of point cloud data, and the point cloud registration needs to find a point-to-point matching relationship between the two frames of point clouds), the transformation matrices corresponding to the two registrations are not different greatly, so that (R, t) is smaller. (R, t) can be regarded as a disturbance, describing the locatable capability of the system, converted into a sensitivity of the laser measurement distance ρ i to (R, t). If the pose of the robot is slightly disturbed and the laser measurement distance is changed greatly, the pose constraint of the robot is strong. Otherwise, if the pose of the robot is disturbed, but the laser measurement distance is not changed greatly, the robot is extremely weak in constraint, and the system is in a degradation environment.
In another preferred embodiment, referring to FIG. 2, the preferred process of S300 is as follows:
S301, solving a bias derivative of a laser measurement distance rho i pair (R, t), and linearizing a derivative problem by using R multiplied by I multiplied by theta × based on the assumption of small angle transformation:
s302, based on the hidden function theorem, establishing an equation as follows:
combining S301 derivation, we can obtain:
Step S303, constructing an F matrix and a T matrix according to the formula obtained in the step S302, wherein the sensitivity of laser constraint to translation parameters and the sensitivity of laser constraint to rotation parameters are respectively represented:
and step S304, carrying out eigenvalue decomposition according to the formula obtained in the step S303 to obtain corresponding eigenvectors.
In step S305, the feature vector corresponding to the minimum feature value is the current degradation direction, the minimum feature value of the F matrix is used for judging the degradation direction of the translation amount, and the minimum feature value of the T matrix is used for judging the degradation direction of the rotation.
The projection of any one of the eigenvectors on the coordinate system (matrix) is only the expansion and contraction of the fixed, and the expansion and contraction proportion is the magnitude of the eigenvalue, so that the eigenvalue can reflect the proportion of the vector direction on the matrix, and the smaller the eigenvalue, the smaller the constraint of the matrix on the eigenvector direction is. The constraint is small, indicating that degradation is likely to occur in this direction, so the minimum eigenvalue λ min can describe the locatability of the current position.
Further, if λ min is smaller than the set threshold, it represents that the current scene is degraded. At the same time, the feature vector corresponding to the minimum feature value λ min is the current degradation direction.
Based on the same technical conception, in other embodiments of the present invention, a laser radar SLAM degradation detection system based on pose constraint is provided, which includes a data module, a constraint relation module, and a disturbance recognition module. The robot pose disturbance recognition system comprises a data module, a constraint relation module and a disturbance recognition module, wherein the data module acquires continuous frame point clouds through a laser radar sensor, the constraint relation module analyzes the continuous frame point clouds to acquire a point cloud constraint relation of robot pose disturbance rejection robustness, and the disturbance recognition module performs robot pose constraint disturbance recognition according to the point cloud constraint relation.
Based on the same technical concept, in other embodiments of the present invention, a terminal is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program and is used to perform the above method or to run the above system.
Optionally, the memory is used for storing programs, and may include volatile memory (english) such as random-access memory (RAM), static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (Double Data Rate Synchronous Dynamic Random Access Memory, DDR SDRAM), and the like, and nonvolatile memory (non-volatile memory) such as flash memory (english). The memory is used to store computer programs (e.g., application programs, functional modules, etc. that implement the methods described above), computer instructions, etc., which may be stored in one or more memories in a partitioned manner. And the above-described computer programs, computer instructions, data, etc. may be invoked by a processor.
The computer programs, computer instructions, etc. described above may be stored in one or more memories in partitions. And the above-described computer programs, computer instructions, data, etc. may be invoked by a processor.
A processor for executing the computer program stored in the memory to implement the steps in the method according to the above embodiment. Reference may be made in particular to the description of the embodiments of the method described above.
The processor and the memory may be separate structures or may be integrated structures that are integrated together. When the processor and the memory are separate structures, the memory and the processor may be connected by a bus coupling.
Based on the same technical idea, in other embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is operative to perform the method described above, or to run the system described above.
Among them, computer-readable media include computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in a user device. The processor and the storage medium may reside as discrete components in a communication device.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the claims without affecting the spirit of the invention. The above-described preferred features may be used in any combination without collision.
Claims (7)
1. The laser radar SLAM degradation detection method based on pose constraint is characterized by comprising the following steps of:
collecting continuous frame point clouds through a laser radar sensor;
Analyzing the continuous frame point cloud to obtain a point cloud constraint relation of robot pose disturbance rejection robustness;
According to the point cloud constraint relation, carrying out robot pose constraint disturbance recognition to obtain an SLAM degradation direction;
analyzing the continuous frame point cloud to obtain a point cloud constraint relation of robot pose disturbance rejection robustness, wherein the method comprises the following steps:
Approximating the local surface of the continuous frame point cloud as a plane, each measurement point satisfying the following relation:
Wherein, the Is a unit vector of a laser beam under a laser radar coordinate system and is used for indicating the direction of the laser beam,Corresponding to the laser measurement distance, i e {1,2,..m } is the index of the laser beam,Is a normal vector of the approximate plane, p i,0 is a point on the approximate plane,Describing the pose of the robot, wherein R is a rotation matrix, and t is a translation distance;
Will- And d i, obtaining a point cloud constraint relation of the robot pose disturbance rejection robustness as follows:
the robot pose constraint disturbance recognition is carried out according to the point cloud constraint relation, and the method comprises the following steps:
in the point cloud registration process, the variation of the pose (R, t) of the robot is regarded as disturbance;
judging whether the laser radar SLAM is subject to scene degradation or not by calculating the sensitivity degree of the laser measurement distance rho i to (R, t) disturbance;
If the pose of the robot is disturbed and the laser measurement distance is changed greatly, the pose constraint of the robot is described to be strong currently, otherwise, if the pose of the robot is disturbed and the laser measurement distance is not changed greatly, the pose of the robot is described to be weak currently and the system is in a degradation environment.
2. The method for detecting laser radar SLAM degradation based on pose constraint according to claim 1, wherein the obtaining the SLAM degradation direction comprises:
Based on the assumption of small angle theta transformation in robot pose, the derivative problem is linearized by using R (approximately) I (approximately) theta ×:
Based on the hidden function theorem, an equation is established:
The formula is derived from the formula,
According to the equation and the formula obtained by the hidden function theorem, an F matrix and a T matrix are constructed, wherein the sensitivity of the laser constraint to the translation parameter and the sensitivity of the laser constraint to the rotation parameter are respectively represented:
Performing eigenvalue decomposition on the F matrix and the T matrix to obtain a group of eigenvectors,
The feature vector corresponding to the minimum feature value is the current degradation direction, wherein the minimum feature value of the F matrix is used for judging the degradation direction of the translation quantity, and the minimum feature value of the T matrix is used for judging the degradation direction of the rotation.
3. The laser radar SLAM degradation detection method based on pose constraint according to claim 2, wherein the projection of the matrix on any feature vector is the expansion and contraction of the feature vector, the expansion and contraction proportion is a feature value, the feature value reflects the proportion of the projection of the matrix on the direction of the feature vector, and the smaller the feature value, the smaller the constraint of the matrix on the direction of the feature vector is, the smaller the constraint is, and the degradation is easy to occur in the direction.
4. The method for detecting laser radar SLAM degradation based on pose constraint according to claim 3, wherein if the minimum eigenvalue lambda min is smaller than a set threshold value, the minimum eigenvalue lambda min is used for representing degradation of a current scene, and meanwhile, the eigenvector corresponding to the minimum eigenvalue lambda min is the current degradation direction.
5. A laser radar SLAM degradation detection system based on pose constraints, using the laser radar SLAM degradation detection method according to any one of claims 1-4, comprising:
the data module is used for collecting continuous frame point clouds through a laser radar sensor;
The constraint relation module analyzes the continuous frame point clouds and obtains a point cloud constraint relation of robot pose disturbance rejection robustness;
and the disturbance recognition module is used for carrying out robot pose constraint disturbance recognition according to the point cloud constraint relation.
6. A terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor is operable to perform the method of any of claims 1-3 when the program is executed by the processor.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor is operable to perform the method of any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310207645.1A CN116299536B (en) | 2023-03-06 | 2023-03-06 | Laser radar SLAM degradation detection method and system based on pose constraint |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310207645.1A CN116299536B (en) | 2023-03-06 | 2023-03-06 | Laser radar SLAM degradation detection method and system based on pose constraint |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116299536A CN116299536A (en) | 2023-06-23 |
| CN116299536B true CN116299536B (en) | 2025-07-25 |
Family
ID=86781009
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310207645.1A Active CN116299536B (en) | 2023-03-06 | 2023-03-06 | Laser radar SLAM degradation detection method and system based on pose constraint |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116299536B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258600A (en) * | 2020-10-19 | 2021-01-22 | 浙江大学 | Simultaneous positioning and map construction method based on vision and laser radar |
| CN112862894A (en) * | 2021-04-12 | 2021-05-28 | 中国科学技术大学 | Robot three-dimensional point cloud map construction and expansion method |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10353053B2 (en) * | 2016-04-22 | 2019-07-16 | Huawei Technologies Co., Ltd. | Object detection using radar and machine learning |
| US11532168B2 (en) * | 2019-11-15 | 2022-12-20 | Nvidia Corporation | Multi-view deep neural network for LiDAR perception |
| CN115061143B (en) * | 2022-06-22 | 2025-11-25 | 上海西井科技股份有限公司 | LiDAR-based positioning methods, systems, devices, and storage media |
| CN115248439B (en) * | 2022-07-26 | 2025-05-09 | 中山大学 | Laser radar SLAM method and system based on geometric information and intensity information |
| CN115494518B (en) * | 2022-08-15 | 2025-05-09 | 杭州羽亿创智科技有限公司 | A mapping method and laser radar device based on mode switching |
-
2023
- 2023-03-06 CN CN202310207645.1A patent/CN116299536B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258600A (en) * | 2020-10-19 | 2021-01-22 | 浙江大学 | Simultaneous positioning and map construction method based on vision and laser radar |
| CN112862894A (en) * | 2021-04-12 | 2021-05-28 | 中国科学技术大学 | Robot three-dimensional point cloud map construction and expansion method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116299536A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102581263B1 (en) | Method, apparatus, computing device and computer-readable storage medium for positioning | |
| JP2023021098A (en) | Map construction method, apparatus, and storage medium | |
| US8295547B1 (en) | Model-based feature tracking in 3-D and 2-D imagery | |
| US9251587B2 (en) | Motion estimation utilizing range detection-enhanced visual odometry | |
| Rapp et al. | Probabilistic ego-motion estimation using multiple automotive radar sensors | |
| US12158352B2 (en) | Vehicle and method for generating map corresponding to three-dimensional space | |
| CN114236564B (en) | Method for positioning robot in dynamic environment, robot, device and storage medium | |
| KR102624644B1 (en) | Method of estimating the location of a moving object using vector map | |
| CN117495968B (en) | Mobile robot pose tracking method and device based on 3D laser radar | |
| CN112731337B (en) | Map construction method, device and equipment | |
| WO2024001649A1 (en) | Robot positioning method, apparatus and computing readable storage medium | |
| CN112767545A (en) | Point cloud map construction method, device, equipment and computer storage medium | |
| CN112964291A (en) | Sensor calibration method and device, computer storage medium and terminal | |
| CN113483762A (en) | Pose optimization method and device | |
| US12535322B2 (en) | System and methods of vehicle pose estimation | |
| CN112965076A (en) | Multi-radar positioning system and method for robot | |
| CN116299535A (en) | Laser radar SLAM degradation detection method and system based on geometric information | |
| CN116524014B (en) | Method and device for calibrating external parameters on line | |
| CN116203579B (en) | Laser radar SLAM degradation detection method and system based on point cloud residual error | |
| CN116465393A (en) | Synchronous positioning and mapping method and device based on area array laser sensor | |
| CN116299536B (en) | Laser radar SLAM degradation detection method and system based on pose constraint | |
| CN120930082A (en) | AGV accurate positioning control method, device, equipment and medium integrating vision and laser radar | |
| US11210535B1 (en) | Sensor fusion | |
| CN120318316A (en) | A method for autonomous coal handling by unmanned aerial vehicle based on laser radar | |
| CN115128620A (en) | Method, device and equipment for determining position of object |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |