Laser radar calibration method and device, medium and product

Abstract

The embodiment of the invention relates to the technical field of radar calibration, and discloses a laser radar calibration method and device, a medium and a product. The method comprises the following steps: acquiring calibration data through RTK equipment and a calibration board, selecting a plurality of calibration positions in a laser radar visual field range, and recording an RTK coordinate and a laser radar point cloud picture of each calibration position; according to the laser radar point cloud picture, a calibration plate point cloud is extracted; calculating a minimum bounding rectangle of the point cloud of the calibration plate, and determining a center point coordinate of the calibration plate based on a diagonal intersection point of the minimum bounding rectangle; calculating a rotation matrix and a translation matrix between a laser radar coordinate system and a geodetic coordinate system according to the center point coordinate and the RTK coordinate; and calibrating the laser radar according to the rotation matrix and the translation matrix. The technical problem that the manual calibration precision of the laser radar is low can be at least solved.

Description

Technical Field

[0001] This application relates to the field of radar calibration technology, and in particular to a lidar calibration method, equipment, medium and product. Background Technology

[0002] In intelligent transportation, autonomous driving, and other vehicle-road cooperative applications, LiDAR has become one of the key sensing devices due to its high-precision and high-resolution sensing capabilities. However, in order to enable the measurement data provided by LiDAR to be accurately applied in a unified coordinate system, such as integrating it with other sensor data or achieving precise positioning in a geodetic coordinate system, the transformation and calibration between the LiDAR coordinate system and the geodetic coordinate system is an indispensable step.

[0003] Traditional lidar calibration methods typically involve manually selecting calibration points. Specifically, field technicians use a handheld RTK (Real-Time Kinematic) device to record geodetic coordinates at multiple locations within the lidar's field of view, simultaneously acquiring lidar point cloud data. Then, by manually selecting calibration points in the lidar point cloud that correspond to the RTK records, multiple sets of point correspondences are established. Calibration parameters are then calculated to calibrate the lidar to the geodetic coordinate system. Due to the sparsity of lidar point clouds and varying operator experience levels, manual point selection is prone to introducing subjective errors, directly impacting the accuracy of the calibration results. Furthermore, the calibration process is time-consuming, especially in complex scenarios or applications requiring multiple calibrations, where efficiency becomes a significant issue. Summary of the Invention

[0004] One objective of this application is to provide a lidar calibration method, device, medium, and product, at least to solve the technical problem of low accuracy in manual lidar calibration.

[0005] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0006] In a first aspect, some embodiments of this application also provide a lidar calibration method, including collecting calibration data through an RTK device and a calibration board; selecting multiple calibration locations within the lidar's field of view; recording the RTK coordinates and lidar point cloud map of each calibration location; extracting the calibration board point cloud based on the lidar point cloud map; calculating the minimum bounding rectangle of the calibration board point cloud; determining the center point coordinates of the calibration board based on the intersection of the diagonals of the minimum bounding rectangle; calculating the rotation matrix and translation matrix between the lidar coordinate system and the geodetic coordinate system based on the center point coordinates and the RTK coordinates; and calibrating the lidar based on the rotation matrix and the translation matrix.

[0007] Secondly, some embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method described above.

[0008] Thirdly, some embodiments of this application also provide a computer-readable medium having computer program instructions stored thereon, which can be executed by a processor to implement the method described above.

[0009] Fourthly, some embodiments of this application also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described above.

[0010] Compared with related technologies, the solution provided in this application effectively reduces the error caused by manual point selection through automated calibration point extraction and precise mathematical algorithms, significantly improving calibration accuracy and efficiency. At the same time, by filtering and clustering point cloud data, the method enhances its adaptability in complex scenarios, overcomes the dependence of traditional methods on human experience and environmental conditions, and can meet the high-precision requirements of lidar calibration in multiple scenarios. Attached Figure Description

[0011] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0012] Figure 1 This is a flowchart illustrating a lidar calibration method according to an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of the structure of a calibration plate according to an embodiment of this application;

[0014] Figure 3 This is a lidar point cloud map provided according to an embodiment of this application;

[0015] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] First Embodiment

[0018] The first embodiment of this application relates to a lidar calibration method. For example... Figure 1 As shown, the method may include the following steps:

[0019] S101 collects calibration data through RTK equipment and calibration board, selects multiple calibration positions within the field of view of lidar, and records the RTK coordinates and lidar point cloud map of each calibration position;

[0020] S102, Extract the calibration board point cloud based on the lidar point cloud map;

[0021] S103, calculate the minimum bounding rectangle of the calibration board point cloud, and determine the coordinates of the center point of the calibration board based on the intersection of the diagonals of the minimum bounding rectangle;

[0022] S104, Calculate the rotation matrix and translation matrix between the lidar coordinate system and the geodetic coordinate system based on the center point coordinates and the RTK coordinates;

[0023] S105, calibrate the lidar according to the rotation matrix and the translation matrix.

[0024] The following sections will provide a detailed explanation of each of the above steps.

[0025] For step S101: During the calibration process, calibration data is first collected using the RTK device and calibration board. Multiple calibration locations are selected within the lidar's field of view. These locations should be distributed as widely as possible and form a closed area. The on-site operator holds the RTK device, ensuring that the RTK device's measurement points are aligned with the center of the calibration board. At each calibration location, the geodetic coordinate data (i.e., RTK coordinates) collected by the RTK device is recorded, and the point cloud data collected by the lidar is recorded simultaneously. Typically, 6 to 8 calibration locations are selected; the reasonable distribution of these locations plays a crucial role in improving calibration accuracy.

[0026] For step S102: Based on the point cloud data collected by the lidar, extract the point cloud information of the calibration board. Determine the region of interest where the calibration board is located. Since the calibration board is made of a highly reflective material, the location of the calibration board can be initially determined by the reflection intensity value in the lidar point cloud data.

[0027] Select point cloud data from the region of interest. Based on the high reflectivity of the calibration board point cloud, set an intensity threshold to filter out low-reflectivity point clouds and retain high-reflectivity point clouds that may belong to the calibration board. Confirm the extraction results to ensure that the calibration board point cloud is complete and accurate, providing basic data for subsequent steps.

[0028] For step S103: Calculate the coordinates of the center point of the calibration board from the extracted calibration board point cloud data.

[0029] The minimum bounding rectangle of the calibration board point cloud is calculated. The minimum bounding rectangle is the smallest area rectangle that completely covers the calibration board point cloud. The vertex information of the minimum bounding rectangle is obtained by analyzing the distribution of the point cloud and combining mathematical algorithms.

[0030] The coordinates of the center point of the calibration board are determined based on the intersection of the diagonals of the minimum bounding rectangle. The slope and intercept of the diagonals are calculated, and the coordinates of the center point are accurately solved using the formula for the intersection of the two diagonals. This process utilizes the geometric properties of the calibration board's point cloud to ensure the stability and accuracy of the center point coordinate calculation.

[0031] Step S104: Calculate coordinate transformation parameters. Based on the coordinates of the calibration board center point obtained in step S103 and the RTK coordinates recorded in step S101, solve the transformation relationship between the lidar coordinate system and the geodetic coordinate system, including the rotation matrix and translation matrix. The rotation matrix represents the directional relationship between the two coordinate systems. A point-to-point correspondence is established between the RTK coordinates of multiple calibration points and the lidar coordinates. The covariance matrix is ​​solved using mathematical methods, and the rotation matrix is ​​calculated using the Singular Value Decomposition (SVD) method.

[0032] The translation matrix represents the positional offset relationship between two coordinate systems. The translation matrix is ​​calculated based on the rotation matrix and the coordinate data of the calibration point.

[0033] Step S105: Based on the rotation matrix and translation matrix calculated in the above steps, the lidar is calibrated. After calibration, the lidar's coordinate system can be mapped to the geodetic coordinate system, thereby achieving accurate target positioning of the lidar in a unified coordinate system.

[0034] It is readily apparent that, compared to related technologies, the lidar calibration method provided in this application, through automated data processing and precise mathematical calculations, avoids the errors of manual point selection in traditional methods, significantly improving calibration accuracy. Furthermore, through efficient calibration point extraction and coordinate calculation, it reduces the time required for calibration and improves operational efficiency. It is applicable to various complex scenarios, including those with sparse point clouds and significant environmental interference, providing a solid technical guarantee for the further development of vehicle-road cooperative and autonomous driving technologies.

[0035] Second Embodiment

[0036] The second embodiment of this application relates to a lidar calibration method. The second embodiment is an improvement upon the first embodiment, specifically in that:

[0037] like Figure 2 As shown, the calibration board employs a special design, with a high-reflectivity material in its central area and a low-reflectivity material at the edges, facilitating the location of the calibration circle in the point cloud. Since the scene contains other high-reflectivity objects, such as road signs, the region of interest is set to obtain the area where the calibration board is located. For example... Figure 3 As shown, the initial obtained region of interest (ROI) of the calibration board exhibits point clouds of different colors. The central circular region has higher reflectivity, appearing as a bright red, and its color differs significantly from the black region of the calibration board (a large color difference indicates a large difference in the intensity values ​​of the point clouds). By setting a threshold and filtering based on the intensity of the point clouds, the point clouds of high-reflectivity regions are obtained. It can be seen that there are also some red dots on the human body. Because the personnel on site are wearing reflective clothing for safety reasons, it is inevitable that points not on the calibration board will be captured when the threshold is not appropriate. Therefore, the DBSCAN (Density - Based Spatial Clustering of Applications with Noise) clustering algorithm is used to classify the point clouds and obtain densely distributed point cloud clusters with high average reflectivity as the point clouds of the calibration board.

[0038] Extracting the calibration board point cloud includes: obtaining the region of interest (ROI) of the calibration board based on the lidar point cloud map; setting a point cloud reflection threshold for the reflection intensity value of the point cloud, and filtering the ROI of the calibration board based on the point cloud reflection threshold; classifying the filtered point cloud using the DBSCAN clustering algorithm, and extracting the calibration board point cloud.

[0039] Based on the lidar point cloud map, the region of interest (ROI) where the calibration board is located is initially identified by analyzing the spatial distribution and reflection characteristics of the point cloud. During this process, the characteristics of the high-reflectivity central region of the calibration board are given special attention; this region typically exhibits a high point cloud intensity value, forming a clear contrast with the surrounding environment.

[0040] For the high-reflectivity central region of the calibration board, a threshold for the point cloud reflection intensity is set to filter out point cloud data with low reflection intensity, retaining only high-intensity point clouds. This step effectively reduces interference from other low-reflectivity objects or environmental backgrounds on the point cloud extraction from the calibration board. However, it should be noted that since on-site operators may be wearing reflective clothing, high-intensity reflective point clouds on their bodies may also be mistakenly selected, thus requiring further screening and classification.

[0041] The filtered high-intensity point cloud data were classified using the density-based DBSCAN clustering algorithm. This algorithm can identify densely distributed point clouds with high reflectivity as a cluster based on the spatial distribution of the point clouds. Calibration board point clouds typically exhibit dense clusters with consistent high reflectivity characteristics. Therefore, the DBSCAN clustering results can be used to accurately extract calibration board point clouds while eliminating other potentially misselected points (such as point clouds caused by reflective clothing).

[0042] Calculating the minimum bounding rectangle of the calibration board point cloud includes: obtaining the principal direction of the calibration board point cloud using Principal Component Analysis (PCA), rotating the calibration board point cloud to align with the principal direction, and calculating the vertex information of the minimum bounding rectangle.

[0043] The point cloud of the calibration board is not a complete circular region. This is because the resolution of the lidar is limited, and the point cloud of the calibration board becomes sparser as the distance between the calibration board and the lidar increases. By observing the point cloud of the calibration board at different positions, it is found that the point cloud is basically symmetrical with respect to the center of the circle. The minimum bounding rectangle of the calibration board can be solved, and the intersection of the diagonals of the minimum bounding rectangle can be used to approximate the coordinates of the center of the calibration board.

[0044] PCA extracts the principal direction (i.e., the direction of maximum variance) and secondary direction (i.e., the direction of minimum variance) of the point cloud distribution by calculating the covariance matrix of the point cloud data. The principal direction represents the main extension direction of the calibration board point cloud and is an important reference for calculating the minimum bounding rectangle.

[0045] Based on the principal direction extracted using the PCA method, the calibration board point cloud is rotated to align its principal direction with the coordinate axes. After the point cloud is rotated and aligned, the four vertex information of the minimum bounding rectangle is calculated. The minimum bounding rectangle is the rectangle that covers all calibration board point cloud data, has the smallest area, and is consistent with the point cloud distribution direction. By projecting the rotated and aligned point cloud, the boundary points and vertex coordinates of the minimum bounding rectangle can be accurately determined.

[0046] Since the point cloud undergoes rotation during computation, the vertex information of the smallest bounding rectangle must be restored to the original coordinate system to ensure coordinate consistency in subsequent steps. This is achieved by using an inverse rotation matrix to transform the rectangle vertices from the rotation-aligned coordinate system back to the original coordinate system of the LiDAR.

[0047] Determining the center point coordinates of the calibration board includes: obtaining the vertex coordinates of the diagonal of the minimum bounding rectangle based on the vertex information, and calculating the center point coordinates (x, y, z) according to the following formula:

[0048] ;

[0049] ;

[0050] ;

[0051] in, The coordinates of the first vertex of the diagonal; The coordinates are the coordinates of the second vertex of the diagonal.

[0052] The calculation of the rotation and translation matrices between the lidar coordinate system and the geodetic coordinate system includes the transformation formula:

[0053] ;

[0054] Where W is the RTK coordinate, L is the center point coordinate, R is the rotation matrix, and t is the translation matrix.

[0055] The rotation matrix includes: subtracting the coordinates of the center point of multiple calibration positions from the RTK coordinates to calculate the covariance matrix; performing singular value decomposition (SVD) on the covariance matrix; and calculating the rotation matrix based on the decomposition results.

[0056] The translation matrix includes: substituting the rotation matrix into the transformation formula to calculate the translation matrix.

[0057] In the lidar coordinate system, the coordinates of the calibration board center point at multiple calibration locations are extracted using point cloud processing. These coordinates are obtained by computing the reflection points of the calibration board within the lidar's field of view. In the geodetic coordinate system, the coordinates of the calibration board center point at each calibration location are recorded using an RTK device. These coordinates, based on the high-precision positioning function of the RTK device, accurately represent the actual position of the calibration board in the geodetic coordinate system.

[0058] In the lidar coordinate system, the offset represents the relative position of each point within the point cloud distribution; in the geodetic coordinate system, the offset represents the relative position of each point within the global distribution. The offset reflects the spatial distribution characteristics of the calibration point relative to its center point. By comparing and analyzing the two sets of offsets, the directional relationship between the two coordinate systems can be revealed, providing the necessary information for calculating the rotation matrix.

[0059] Based on the offset data, a covariance matrix is ​​constructed. This matrix integrates the correlation between the calibration point distribution in the lidar coordinate system and the geodetic coordinate system, and is the core of the subsequent calculation of the rotation matrix. Through matrix decomposition, the directional transformation relationship between the lidar coordinate system and the geodetic coordinate system can be extracted.

[0060] The results of singular value decomposition (SVD) can extract the main orientation of the point cloud distribution. Based on the SVD results, the rotation matrix can be directly calculated through the product of orthogonal matrices. The rotation matrix describes the directional change relationship between the lidar coordinate system and the geodetic coordinate system.

[0061] The translation matrix represents the relative displacement between the center point of the lidar coordinate system and the center point of the geodetic coordinate system, describing the spatial positional difference between the two coordinate systems.

[0062] The translation matrix is ​​calculated based on the rotation matrix and the calibration points in the two coordinate systems. First, the center point in the lidar coordinate system is rotated to the direction of the geodetic coordinate system using the rotation matrix. Then, the center point of the geodetic coordinate system is compared with the rotated center point of the lidar to obtain the translation matrix.

[0063] Optionally, check if the rotation matrix satisfies the orthogonality condition, i.e., whether the product of its transpose and itself is an identity matrix. Use the calculated rotation and translation matrices to transform the calibration points and verify the accuracy of their correspondence in the two coordinate systems. Evaluate the accuracy of the calibration results by comparing the deviations between the actual coordinates and the transformed coordinates.

[0064] It is easy to see that in this embodiment, the DBSCAN clustering algorithm is used to automatically extract the calibration board point cloud; the PCA method and minimum bounding rectangle are used to calculate the center point of the calibration board; and the rotation and translation matrices are accurately solved through SVD decomposition, thus improving the overall accuracy of the calibration process. The calibration process is automated, requiring no manual intervention and significantly shortening the calibration time. The point cloud intensity screening and clustering algorithm quickly locates the calibration board region, reducing interference from other highly reflective objects and adapting to complex scenes, such as sparse point cloud regions, reflective interference objects, or various environmental conditions.

[0065] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0066] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0067] The electronic device includes: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 4As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0068] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0069] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0070] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0071] In this embodiment, a computer-readable medium stores a computer program / instructions that, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0072] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0073] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0074] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0075] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0076] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0077] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an Application-Specific Integrated Circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Additionally, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0078] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line, DSL) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk, SSD), etc.

[0079] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0080] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method of laser radar calibration, characterized by, The method comprises: Collecting calibration data by an RTK device and a calibration board, selecting multiple calibration positions within the field of view of the laser radar, and recording the RTK coordinates and the laser radar point cloud map of each calibration position; According to the laser radar point cloud map, the calibration board point cloud is extracted; The minimum bounding rectangle of the calibration board point cloud is calculated, and the center point coordinates of the calibration board are determined based on the intersection of the diagonals of the minimum bounding rectangle; According to the center point coordinates and the RTK coordinates, the rotation matrix and the translation matrix between the laser radar coordinate system and the geodetic coordinate system are calculated; and the laser radar is calibrated according to the rotation matrix and the translation matrix.

2. The method of claim 1, wherein, The extraction of the calibration board point cloud comprises: Obtaining the region of interest of the calibration board according to the laser radar point cloud map; Setting a point cloud reflection threshold for the reflection intensity value of the point cloud, and screening the region of interest of the calibration board according to the point cloud reflection threshold; Classifying the screened point cloud by using the DBSCAN clustering algorithm to extract the calibration board point cloud.

3. The method of claim 2, wherein, The calculation of the minimum bounding rectangle of the calibration board point cloud comprises: Using the PCA method to obtain the principal direction of the calibration board point cloud, rotating the calibration board point cloud to align with the principal direction, and calculating the vertex information of the minimum bounding rectangle.

4. The method of claim 3, wherein, The determination of the center point coordinates of the calibration board comprises: According to the vertex information, the vertex coordinates of the diagonal of the minimum bounding rectangle are obtained, and the center point coordinates (x, y, z) are calculated according to the following formula: ； ； ； wherein is a first vertex coordinate of the diagonal line; is a second vertex coordinate of the diagonal line.

5. The method of claim 4, wherein, The calculation of the rotation matrix and the translation matrix between the laser radar coordinate system and the geodetic coordinate system comprises: The conversion formula is: ； Wherein, W is the RTK coordinates, L is the center point coordinates, R is the rotation matrix, and t is the translation matrix.

6. The method of claim 5, wherein, The rotation matrix comprises: The center point coordinates and the RTK coordinates of the multiple calibration positions are subtracted to calculate the covariance matrix; The singular value decomposition is performed on the covariance matrix, and the rotation matrix is calculated according to the decomposition result.

7. The method of claim 6, wherein, The translation matrix comprises: The rotation matrix is substituted into the conversion formula to calculate the translation matrix.

8. An electronic device, comprising: The electronic device comprises: One or more processors; and Memory storing computer program instructions which, when executed, cause the processor to perform the steps of the method of any one of claims 1 to 7.

9. A computer readable medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method of any one of claims 1 to 7.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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