WO2024197815A1 - Procédé et dispositif de mappage de machine d'ingénierie, et support de stockage lisible - Google Patents

Procédé et dispositif de mappage de machine d'ingénierie, et support de stockage lisible Download PDF

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WO2024197815A1
WO2024197815A1 PCT/CN2023/085476 CN2023085476W WO2024197815A1 WO 2024197815 A1 WO2024197815 A1 WO 2024197815A1 CN 2023085476 W CN2023085476 W CN 2023085476W WO 2024197815 A1 WO2024197815 A1 WO 2024197815A1
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point cloud
current frame
laser
point
imu
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Chinese (zh)
Inventor
任好玲
吴江东
林添良
缪骋
姚瑜
张春晖
林元正
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Huaqiao University
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Huaqiao University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
    • 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
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • 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
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • 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

Definitions

  • the present invention relates to the field of motion mapping, and in particular to a mapping method, device, and readable storage medium for engineering machinery.
  • Construction machinery is widely used in the field of engineering construction. With the continuous development of global science and technology, the intelligentization of construction machinery has become a future development trend.
  • the intelligentization of construction machinery includes product and construction intelligence, manufacturing intelligence, service intelligence and management intelligence.
  • the unmanned driving of construction machinery belongs to the category of product and construction intelligence, which means that the construction machinery can autonomously complete the corresponding walking and operation tasks without human intervention.
  • unmanned construction machinery includes five modules: environmental perception, vehicle positioning, decision-making and planning, motion control, and automatic operation.
  • vehicle positioning is the basis for achieving decision-making and planning, motion control, and automatic operation.
  • construction machinery has complex working conditions and harsh operating environments.
  • a single sensor cannot meet the high-precision and high-robustness positioning requirements of construction machinery.
  • unmanned vehicle technology is mainly used in the automotive field, and there is relatively little research on related technologies in the field of construction machinery.
  • Existing unmanned construction machinery vehicle positioning technology is mainly based on single sensor positioning, which has problems such as slow mapping speed, easy degradation of mapping, and low positioning robustness.
  • the present invention discloses a construction method, device, and readable storage medium for construction machinery, aiming to solve the problems of slow construction speed, easy degradation of construction, and low positioning robustness of construction machinery.
  • a first embodiment of the present invention provides a method for mapping an engineering machine, comprising:
  • factor graph optimization is performed on the laser point cloud of the current frame, and the laser point cloud of the current frame is aligned with the local map composed of historical frames using a normal distribution transformation algorithm to optimize and correct the global pose.
  • the environmental information collected by the vehicle-mounted multi-sensor fusion platform is acquired, and the motion distortion correction of the current frame laser point cloud of the environmental information is performed using IMU data and an IMU odometer, specifically:
  • the coordinate transformation matrix is used to perform motion distortion correction on the laser point cloud of the current frame; wherein the coordinate transformation matrix is:
  • extracting point-line features and point-surface features according to the corrected curvature of each point of the laser point cloud of the current frame, and matching the point-line features and the point-surface features with the local map is specifically as follows:
  • the curvature calculation model is called to generate the curvature of each point of the laser point cloud of the current frame.
  • the curvature calculation model is as follows:
  • K is the curvature of the i-th point of the laser point cloud
  • d i is the distance from the i-th point to the origin of the laser radar coordinate system
  • d i+j -5, -4, ..., 5 is the distance from the five laser points before and after the i-th point to the origin of the laser radar coordinate system
  • the kd tree is used to find the points closest to the corner point features in the feature point cloud of the current frame in the local map and connect them into a line, and the distance from the corner point in the feature point cloud of the current frame to the line is generated.
  • the calculation model is as follows:
  • the kd tree is used to find the five points closest to it in the local map, and the following plane equation is constructed:
  • A, B, C, and D are the coefficients of the plane equation, and x, y, and z are the coordinates of the five points in the local map that are closest to the surface features of the feature point cloud of the current frame;
  • x [ xp , yp , zp ] T is the coordinate of the surface point in the feature point cloud of the current frame in the world coordinate system;
  • J is the Jacobian matrix of the residual equation relative to the iteration variable
  • ⁇ is the diagonal update operator
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • m is the local map in the world coordinate system
  • d ⁇ i is the residual value between the i-th corner point of the feature point cloud of the current frame and the corresponding edge feature of the local map
  • d Hj is the residual value between the j-th surface point of the feature point cloud of the current frame and the corresponding plane feature of the local map
  • m and n are the number of corner points and surface points in the feature point cloud of the current frame respectively;
  • the diagonal update operator is updated according to the residual sum of feature matching. When the residual sum is too large, the diagonal operator is increased, otherwise the diagonal operator is reduced.
  • the IMU zero bias is corrected and the IMU pre-integrator is reset, specifically:
  • a pre-integrator for optimizing the IMU zero bias and a pre-integrator for calculating the IMU odometer are defined respectively;
  • the pose and velocity estimated by the pre-integrator for optimizing the IMU zero bias are used as the estimated values of the pose factor and velocity factor of the current frame, and the zero bias optimized in the previous frame is used as the estimated value of the zero bias factor of the current frame, and the factor graph is optimized to generate the optimized and updated velocity and zero bias;
  • the state of the laser point cloud of the current frame obtained by optimizing the factor graph and the IMU zero bias are used to estimate the state after the laser point cloud of the current frame in combination with the pre-integration result, and the estimate is output as a high-frequency odometer.
  • the factor graph optimization is performed on the laser point cloud of the current previous frame
  • the normal distribution transformation algorithm is used to align the laser point cloud of the current frame with the local map composed of the historical frames to optimize and correct the global posture, specifically:
  • the loop detection thread is called based on the laser odometer
  • the global pose is optimized and corrected using the relative coordinate transformation matrix.
  • the normal distribution transformation algorithm is used to match the current key frame with the similar historical key frame to generate a relative coordinate transformation matrix, specifically:
  • the similar historical key frames are used as target point clouds for rasterization.
  • the probability density function of the laser point cloud distribution of each grid is calculated.
  • the calculation model is as follows:
  • D is the vector dimension, is the mean vector, ⁇ is the covariance matrix of the random vector;
  • the target point cloud is placed in each grid, and the mean of the laser point cloud data in each grid is calculated.
  • the calculation model is as follows:
  • the covariance calculation formula is used to calculate the covariance of the point cloud data in each grid.
  • the calculation model is as follows:
  • the current key frame is placed in each grid as the source point cloud for point cloud registration using the normal distribution transformation algorithm.
  • the probability of each point in the source point cloud being in each grid is calculated.
  • the calculation model is as follows:
  • the maximum likelihood function is obtained by multiplying the probability of each point in the source point cloud in each grid.
  • the relative coordinate transformation matrix at this time is the optimal solution, and its calculation model is as follows:
  • R 3 ⁇ 3 is the rotation matrix of the source point cloud relative to the target point cloud
  • t 3 ⁇ 1 is the translation matrix of the source point cloud relative to the target point cloud
  • T is the coordinate transformation matrix of the source point cloud relative to the target point cloud.
  • the Newton iteration method is used to solve the maximum value of the maximum likelihood function.
  • the coordinate transformation matrix of the source point cloud relative to the target point cloud is used as the iteration variable.
  • the coordinate transformation matrix corresponding to the maximum value of the maximum likelihood function is solved by continuously updating the iteration variable in the direction of gradient descent.
  • H is the Hessian matrix of the maximum likelihood function
  • T is the coordinate transformation matrix of the source point cloud relative to the target point cloud.
  • a second embodiment of the present invention provides a mapping device for engineering machinery, comprising:
  • a distortion correction unit is used to obtain environmental information collected by the vehicle-mounted multi-sensor fusion platform, and use IMU data and IMU odometer to perform motion distortion correction on the current frame laser point cloud of the environmental information;
  • a map matching unit used for extracting point-line features and point-surface features according to the corrected curvature of each point of the laser point cloud of the current frame, and matching the point-line features and the point-surface features with a local map;
  • a judging unit used to judge whether the laser point cloud of the current frame is a key frame
  • IMU correction unit used to correct the IMU zero bias and reset the IMU pre-integrator
  • the global optimization unit is used to optimize the factor graph of the laser point cloud of the current frame, and to register the laser point cloud of the current frame with the local map composed of the historical frames by using the normal distribution transformation algorithm, so as to optimize and correct the global posture.
  • a third embodiment of the present invention provides a mapping device for engineering machinery, including a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement a mapping method for engineering machinery as described in any one of the above items.
  • a fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for mapping an engineering machine as described in any one of the above.
  • a mapping method, device, and readable storage medium for engineering machinery are provided.
  • the key points of the technical solution are to use IMU data and IMU odometer to correct the motion distortion of the current frame laser point cloud, extract point line and point surface features according to the curvature of each point of the current frame laser point cloud, match the local map with the current frame laser point cloud, and optimize the key frame by factor graph.
  • the output laser odometer the IMU zero bias is corrected, the IMU pre-integrator is reset, and the IMU odometer is output.
  • the normal distribution transformation algorithm is used to perform loop detection on the current frame laser point cloud, and the global pose is updated, which solves the mapping problem in the degraded scene of engineering machinery and realizes rapid mapping and positioning in large scenes.
  • FIG1 is a schematic flow chart of a method for mapping an engineering machine provided in accordance with a first embodiment of the present invention
  • first ⁇ second mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first ⁇ second” can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by “first ⁇ second” can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.
  • the present invention discloses a construction method, device, and readable storage medium for construction machinery, aiming to solve the problems of slow construction speed, easy degradation of construction, and low positioning robustness of construction machinery.
  • a first embodiment of the present invention provides a method for mapping an engineering machine, which can be executed by a mapping device of the engineering machine, and in particular, by one or more processors in the mapping device, to implement at least the following steps:
  • the mapping device can be a terminal with data processing and analysis capabilities, such as a desktop computer, a laptop computer, a server, a workstation, etc., wherein the mapping device can be installed with a corresponding operating system and application software, and the functions required by this embodiment are realized through the combination of the operating system and the application software; wherein the multi-sensor fusion platform can include IMU sensors, lidars, laser odometers and other sensor devices.
  • a frame of laser radar point cloud data is obtained by scanning a circle (360°) of the laser radar, but during the scanning cycle, the vehicle is also moving at high speed. This results in inaccurate values of the point cloud coordinates obtained at different scanning angles in the scanning starting coordinate system;
  • the IMU data can be pre-integrated first to obtain the coordinate transformation matrix of the laser radar from the end scanning moment to the start scanning moment of the laser point cloud of the current frame, wherein the transformation matrix is equivalent to the rotation and translation of the whole vehicle at each scanning moment relative to the start scanning moment, and then the rotation and translation are converted into a 4*4 homogeneous coordinate transformation matrix.
  • the coordinate transformation matrix is used to perform motion distortion correction on the laser point cloud of the current frame; wherein the coordinate transformation matrix is:
  • the map itself is composed of features extracted from each frame.
  • the purpose of matching is to obtain the coordinate transformation matrix of the current frame relative to the map.
  • the coordinate transformation matrix can be used to accurately splice the current frame onto the global map.
  • the curvature calculation model is called to generate the curvature of each point of the laser point cloud of the current frame.
  • the curvature calculation model is as follows:
  • K is the curvature of the i-th point of the laser point cloud
  • d i is the distance from the i-th point to the origin of the laser radar coordinate system
  • d i+j -5, -4, ..., 5 is the distance from the five laser points before and after the i-th point to the origin of the laser radar coordinate system
  • the kd tree is used to find the points closest to the corner point features in the feature point cloud of the current frame in the local map and connect them into a line, and the distance from the corner point in the feature point cloud of the current frame to the line is generated.
  • the calculation model is as follows:
  • A, B, C, and D are the coefficients of the plane equation, and x, y, and z are the coordinates of the five points in the local map that are closest to the surface features of the feature point cloud of the current frame;
  • x [ xp , yp , zp ] T is the coordinate of the surface point in the feature point cloud of the current frame in the world coordinate system;
  • J is the Jacobian matrix of the residual equation relative to the iteration variable
  • ⁇ is the diagonal update operator
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • I is the identity matrix
  • ⁇ T is the increment of the single iteration variable
  • f is the residual equation
  • m is the local map in the world coordinate system
  • d ⁇ i is the residual value between the i-th corner point of the feature point cloud of the current frame and the corresponding edge feature of the local map
  • d Hj is the residual value between the j-th surface point of the feature point cloud of the current frame and the corresponding plane feature of the local map
  • m and n are the number of corner points and surface points in the feature point cloud of the current frame respectively;
  • the diagonal update operator is updated according to the residual sum of feature matching. When the residual sum is too large, the diagonal operator is increased, otherwise the diagonal operator is reduced.
  • the key frame is determined based on the distance. For example, if the distance between the current frame and the previous key frame exceeds d, the current frame is determined to be a key frame. In this embodiment, it can be determined by the data collected by the laser odometer. Of course, in other embodiments, it can also be determined by other methods. No specific limitations are made here, but these schemes are within the protection scope of the present invention.
  • a pre-integrator for optimizing the IMU zero bias and a pre-integrator for calculating the IMU odometer are defined respectively;
  • the laser odometer and update the factor graph, and determine whether the factor tree in the updated factor graph exceeds a preset value (the preset value can be but is not limited to 100). If so, rebuild the factor graph;
  • the pose and velocity estimated by the pre-integrator for optimizing the IMU zero bias are used as the estimated values of the pose factor and velocity factor of the current frame, and the zero bias optimized in the previous frame is used as the estimated value of the zero bias factor of the current frame, and the factor graph is optimized to generate the optimized and updated velocity and zero bias;
  • the state of the laser point cloud of the current frame obtained by optimizing the factor graph and the IMU zero bias are used to estimate the state after the laser point cloud of the current frame in combination with the pre-integration result, and the estimate is output as a high-frequency odometer.
  • the factor graph is optimized for the laser point cloud of the current frame, and the normal distribution transformation algorithm is used to align the laser point cloud of the current frame with the local map composed of the historical frames, so as to optimize and correct the global posture.
  • the loop detection thread is called based on the laser odometer. It should be noted that even when the current frame is detected to be a key frame, it is still necessary to determine whether to call the loop detection thread based on the laser odometer to avoid loop detection for each key frame, which leads to insufficient computing power.
  • the global pose is optimized and corrected using the relative coordinate transformation matrix.
  • the normal distribution transformation algorithm is used to match the current key frame with the similar historical key frame to generate a relative coordinate transformation matrix, specifically:
  • the similar historical key frames are used as target point clouds for rasterization.
  • the probability density function of the laser point cloud distribution of each grid is calculated.
  • the calculation model is as follows:
  • D is the vector dimension, is the mean vector, ⁇ is the covariance matrix of the random vector;
  • the target point cloud is placed in each grid, and the mean of the laser point cloud data in each grid is calculated.
  • the calculation model is as follows:
  • the covariance calculation formula is used to calculate the covariance of the point cloud data in each grid.
  • the calculation model is as follows:
  • the current key frame is placed in each grid as the source point cloud for point cloud registration using the normal distribution transformation algorithm.
  • the probability of each point in the source point cloud being in each grid is calculated.
  • the calculation model is as follows:
  • the maximum likelihood function is obtained by multiplying the probability of each point in the source point cloud in each grid.
  • the relative coordinate transformation matrix at this time is the optimal solution, and its calculation model is as follows:
  • R 3 ⁇ 3 is the rotation matrix of the source point cloud relative to the target point cloud
  • t 3 ⁇ 1 is the translation matrix of the source point cloud relative to the target point cloud
  • T is the coordinate transformation matrix of the source point cloud relative to the target point cloud.
  • the Newton iteration method is used to solve the maximum value of the maximum likelihood function.
  • the coordinate transformation matrix of the source point cloud relative to the target point cloud is used as the iteration variable.
  • the coordinate transformation matrix corresponding to the maximum value of the maximum likelihood function is solved by continuously updating the iteration variable in the direction of gradient descent.
  • H is the Hessian matrix of the maximum likelihood function
  • T is the coordinate transformation matrix of the source point cloud relative to the target point cloud.
  • a mapping device for engineering machinery comprising:
  • the distortion correction unit 201 is used to obtain the environmental information collected by the vehicle-mounted multi-sensor fusion platform, and perform motion distortion correction on the current frame laser point cloud of the environmental information using IMU data and IMU odometer;
  • a map matching unit 202 is used to extract point-line features and point-surface features according to the corrected curvature of each point of the laser point cloud of the current frame, and match the point-line features and the point-surface features with a local map;
  • a judging unit 203 is used to judge whether the laser point cloud of the current frame is a key frame
  • An IMU correction unit 204 used to correct the IMU zero bias and reset the IMU pre-integrator
  • the global optimization unit 205 is used to perform factor graph optimization on the laser point cloud of the current frame, and to register the laser point cloud of the current frame with the local map formed by the historical frames using the normal distribution transformation algorithm, so as to optimize and correct the global posture.
  • a third embodiment of the present invention provides a mapping device for engineering machinery, including a memory and a processor, wherein a computer program is stored in the memory, and the computer program can be executed by the processor to implement a mapping method for engineering machinery as described in any one of the above items.
  • a fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for mapping an engineering machine as described in any one of the above.
  • the key points of its technical solution are to use IMU data and IMU odometer to correct the motion distortion of the laser point cloud of the current frame, extract point-line and point-surface features according to the curvature of each point of the laser point cloud of the current frame, match the local map with the laser point cloud of the current frame, and optimize the factor graph of the key frame.
  • the IMU zero bias is corrected, the IMU pre-integrator is reset, and the IMU odometer is output.
  • the normal distribution transformation algorithm is used to perform loop detection on the laser point cloud of the current frame, and the global pose is updated, which solves the mapping problem in the degraded scene of the construction machinery and realizes rapid mapping and positioning in large scenes.
  • the computer program described in the third and fourth embodiments of the present invention may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention.
  • the one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the mapping device for implementing a construction machinery.
  • the device described in the second embodiment of the present invention may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention.
  • the one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the mapping device for implementing a construction machinery.
  • the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or any conventional processor, etc.
  • the processor is the control center of the mapping method for a construction machinery, and uses various interfaces and lines to connect the various parts of the entire mapping method for a construction machinery.
  • the memory can be used to store the computer program and/or module, and the processor realizes various functions of a construction machinery mapping method by running or executing the computer program and/or module stored in the memory and calling the data stored in the memory.
  • the memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc.
  • the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (
  • the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium.
  • the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented.
  • the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • electric carrier signal telecommunication signal and software distribution medium.
  • the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.
  • the computer-readable medium does not include electric carrier signal and telecommunication signal.
  • the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.
  • the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without creative work.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Image Processing (AREA)

Abstract

La présente invention concerne un procédé et un dispositif de mappage de machine d'ingénierie, ainsi qu'un support de stockage lisible. Les points clés de la solution technique résident dans : la réalisation d'une correction de distorsion de mouvement sur la trame actuelle d'un nuage de points laser à l'aide de données IMU et d'un odomètre IMU ; l'extraction de caractéristiques de ligne de point et de plan de point en fonction de la courbure de chaque point de la trame actuelle du nuage de points laser, et la mise en correspondance d'une carte locale avec la trame actuelle du nuage de points laser ; la réalisation d'une optimisation de graphe de facteur sur une trame clé ; la correction d'une polarisation IMU selon un odomètre laser émis, la réinitialisation d'un pré-intégrateur IMU et la sortie de l'odomètre IMU ; et lorsque la trame actuelle du nuage de points laser satisfait une condition de détection de fermeture de boucle, la réalisation d'une détection de fermeture de boucle sur la trame actuelle du nuage de points laser à l'aide d'un algorithme de transformation de distribution normale, et la mise à jour d'une pose globale. De cette manière, un problème de mappage dans un scénario de dégradation de machinerie d'ingénierie est résolu, et un mappage et un positionnement rapides dans un grand scénario sont réalisés.
PCT/CN2023/085476 2023-03-24 2023-03-31 Procédé et dispositif de mappage de machine d'ingénierie, et support de stockage lisible Ceased WO2024197815A1 (fr)

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