Fast calibration method and device based on multi-solid-state laser radar, equipment and medium

Abstract

The application particularly relates to a fast calibration method, device and equipment based on a multi-solid-state laser radar and a medium, the method comprising the following steps: receiving a calibration instruction of the multi-solid-state laser radar, acquiring first point cloud data of a main laser radar and second point cloud data of at least one slave laser radar in the multi-solid-state laser radar, obtaining a projection matrix of the at least one slave laser radar to the main laser radar, simultaneously acquiring third point cloud data of at least one auxiliary radar, obtaining a dense point cloud map according to the first point cloud data, the second point cloud data and the third point cloud data, and correcting the projection according to the dense point cloud map, so as to perform fine calibration on the multi-solid-state laser radar according to the corrected projection matrix. Thus, the calibration process is complicated due to manual calibration or manual parameter adjustment in automatic calibration, thereby reducing the calibration efficiency and accuracy. Through the joint calibration process of coarse calibration and fine calibration of multiple radars, the calibration accuracy and unmanned efficiency of the multi-solid-state laser radar are improved.

Description

Technical Field

[0001] This invention relates to the field of lidar calibration technology, and in particular to a rapid calibration method, apparatus, equipment and medium based on multi-solid-state lidar. Background Technology

[0002] With the rapid development of autonomous driving and intelligent transportation, the role of LiDAR sensors in environmental perception is becoming increasingly significant. LiDAR sensors are applied in both roadside and vehicle-mounted scenarios due to their high accuracy and immunity to lighting conditions. In roadside scenarios, LiDAR primarily scans the roadside environment using a static scanner to accurately describe real-time road conditions. The algorithm module processes the real-time point cloud data to achieve real-time obstacle perception. In vehicle-mounted scenarios, LiDAR acts as the vehicle's "high-precision perception eye," scanning the surrounding environment in real-time during the vehicle's journey. The vehicle also needs to process the point cloud data in real-time using LiDAR perception algorithms to perceive obstacle information within the driving area. LiDAR (Light Detection and Ranging) systems are broadly categorized into mechanical LiDAR and solid-state LiDAR based on their scanning methods. Mechanical LiDAR achieves 360° fixed scanning through internal rotating components, offering a 360° panoramic view but suffering from poor reliability, short lifespan, and high cost, hindering mass production. Solid-state LiDAR, on the other hand, integrates optical phased arrays and Flash memory, outputting a laser point cloud within a limited field of view. Its advantages include fast scanning speed, reliability, stability, and low cost, but it suffers from limited scanning angles, sidelobe issues, and high manufacturing complexity. Compared to mechanical LiDAR, solid-state LiDAR, with its stability, reliability, and low cost, is poised to become the future trend in automotive applications. In recent years, both in vehicle and roadside applications, mechanical LiDAR has been gradually evolving towards solid-state LiDAR. Simultaneously, single-LiDAR installations are shifting towards multi-LiDAR setups, and various solid-state LiDAR installation layouts have become commonplace. However, achieving a rapid and high-precision unification of the coordinate system across multiple LiDAR systems remains a challenge in industrial applications due to the varying installation angles of different LiDAR units.

[0003] In related technologies, multi-solid-state lidar calibration schemes are mainly divided into two categories: one is to calibrate multiple lidars using a calibration board in a purely manual manner, and the other is to calibrate multiple lidars using a combination of automatic calibration and manual parameter adjustment.

[0004] However, calibrating multiple LiDARs using a calibration board ignores the non-repetitive scanning of solid-state LiDARs, which requires high levels of manual adjustment and is time-consuming and labor-intensive, and lacks replicability for large-scale applications. The method of automatic calibration plus manual parameter adjustment has low accuracy because automatic calibration is mostly based on matching environmental feature points, and requires secondary manual adjustment, which increases the complexity and cost of the process. This needs to be addressed urgently. Summary of the Invention

[0005] This application provides a rapid calibration method, apparatus, equipment, and medium based on multi-solid-state lidar, to solve the problem of reduced calibration efficiency and accuracy caused by the complexity of the calibration process through manual calibration or automatic calibration with manual parameter adjustment.

[0006] The first aspect of this application provides a rapid calibration method based on multi-solid-state lidar, comprising the following steps:

[0007] Receive calibration commands from multiple solid-state lidar systems;

[0008] Based on the calibration instructions, the multi-solid-state lidar is divided into a master lidar and at least one slave lidar. First point cloud data from the master lidar and second point cloud data from the at least one slave lidar are acquired. The projection matrix from the at least one slave lidar to the master lidar is then obtained based on the first and second point cloud data.

[0009] Acquire third point cloud data from at least one auxiliary radar, and obtain a dense point cloud map based on the first point cloud data, the second point cloud data, and the third point cloud data. Then, correct the projection based on the dense point cloud map to precisely calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0010] According to one embodiment of this application, acquiring first point cloud data of the main lidar and second point cloud data of at least one slave lidar, and obtaining a projection matrix from the at least one slave lidar to the main lidar based on the first point cloud data and the second point cloud data, includes:

[0011] Based on the first point cloud data and the second point cloud data, obtain the target field-of-view feature points of the main lidar and the at least one slave lidar;

[0012] Based on a preset matching algorithm, the target field feature points are iteratively solved to output the projection matrix of at least one laser radar from the main laser radar, so as to coarsely calibrate the multi-solid-state laser radar according to the projection matrix.

[0013] According to one embodiment of this application, obtaining the target field-of-view feature points of the main lidar and the at least one slave lidar includes:

[0014] Obtain the target feature points and target feature lines of the target point cloud scene;

[0015] The target field-of-view feature points of the main lidar and the at least one slave lidar are obtained based on the target feature points and the target feature lines.

[0016] According to one embodiment of this application, the projection matrix is ​​corrected based on the dense point cloud map to precisely calibrate the multi-solid-state lidar based on the corrected projection matrix, including:

[0017] Based on the dense point cloud map, the coarse calibration results of the multi-solid-state lidar are matched with the dense point cloud map.

[0018] Based on the matching results, the projection matrix is ​​corrected by the fine calibration module to finely calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0019] According to one embodiment of this application, the projection matrix is ​​corrected by the fine calibration module based on the matching result, so as to finely calibrate the multi-solid-state lidar based on the corrected projection matrix, including:

[0020] The coordinate transformation of the second point cloud data from the lidar is performed to obtain the coordinate transformation result of the second point cloud data;

[0021] Visualization is performed in the same coordinate system based on the transformation result and the first point cloud data. It is determined whether the first point cloud data in the dense point cloud map matches the transformation result. If they do not match, the projection matrix of at least one lidar is corrected.

[0022] According to the rapid calibration method based on multiple solid-state lidars in this application, a calibration command for multiple solid-state lidars is received. First point cloud data from the main lidar and second point cloud data from at least one slave lidar are acquired to obtain a projection matrix from at least one slave lidar to the main lidar. Simultaneously, third point cloud data from at least one auxiliary lidar are acquired. A dense point cloud map is obtained based on the first, second, and third point cloud data. The projection is then corrected based on the dense point cloud map to precisely calibrate the multiple solid-state lidars using the corrected projection matrix. This solves the problem of complex calibration processes caused by manual calibration or manual parameter adjustment during automatic calibration, which reduces calibration efficiency and accuracy. By combining coarse and fine calibration of multiple lidars, the calibration accuracy and unmanned efficiency of the multiple solid-state lidars are improved.

[0023] A second aspect of this application provides a rapid calibration device based on multiple solid-state lidar, comprising:

[0024] The receiving module is used to receive calibration commands from multiple solid-state lidar systems.

[0025] The acquisition module is configured to, based on the calibration instructions, divide the multi-solid-state lidar into a master lidar and at least one slave lidar, acquire first point cloud data of the master lidar and second point cloud data of the at least one slave lidar, and obtain the projection matrix of the at least one slave lidar to the master lidar based on the first point cloud data and the second point cloud data; and

[0026] The correction module is used to acquire third point cloud data of at least one auxiliary radar, obtain a dense point cloud map based on the first point cloud data, the second point cloud data and the third point cloud data, and correct the projection based on the dense point cloud map, so as to accurately calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0027] According to one embodiment of this application, the acquisition module is specifically used for:

[0028] Based on the first point cloud data and the second point cloud data, obtain the target field-of-view feature points of the main lidar and the at least one slave lidar;

[0029] Based on a preset matching algorithm, the target field feature points are iteratively solved to output the projection matrix of at least one laser radar from the main laser radar, so as to coarsely calibrate the multi-solid-state laser radar according to the projection matrix.

[0030] According to one embodiment of this application, the acquisition module is specifically used for:

[0031] Obtain the target feature points and target feature lines of the target point cloud scene;

[0032] The target field-of-view feature points of the main lidar and the at least one slave lidar are obtained based on the target feature points and the target feature lines.

[0033] According to one embodiment of this application, the correction module is specifically used for:

[0034] Based on the dense point cloud map, the coarse calibration results of the multi-solid-state lidar are matched with the dense point cloud map.

[0035] Based on the matching results, the projection matrix is ​​corrected by the fine calibration module to finely calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0036] According to one embodiment of this application, the correction module is specifically used for:

[0037] The coordinate transformation of the second point cloud data from the lidar is performed to obtain the coordinate transformation result of the second point cloud data;

[0038] Visualization is performed in the same coordinate system based on the transformation result and the first point cloud data. It is determined whether the first point cloud data in the dense point cloud map matches the transformation result. If they do not match, the projection matrix of at least one lidar is corrected.

[0039] According to an embodiment of this application, a rapid calibration device based on multiple solid-state lidars receives calibration instructions from multiple solid-state lidars, acquires first point cloud data from the main lidar and second point cloud data from at least one slave lidar, obtains a projection matrix from at least one slave lidar to the main lidar, and simultaneously acquires third point cloud data from at least one auxiliary lidar. A dense point cloud map is obtained based on the first, second, and third point cloud data, and the projection is corrected based on the dense point cloud map to precisely calibrate the multiple solid-state lidars using the corrected projection matrix. This solves the problem of complex calibration processes caused by manual calibration or manual parameter adjustment during automatic calibration, which reduces calibration efficiency and accuracy. By combining coarse and fine calibration of multiple lidars, the calibration accuracy and unmanned efficiency of the multiple solid-state lidars are improved.

[0040] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the rapid calibration method based on multi-solid-state lidar as described in the above embodiments.

[0041] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the rapid calibration method based on multi-solid-state lidar as described in the above embodiments.

[0042] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0044] Figure 1 This is a flowchart of a rapid calibration method based on multiple solid-state lidars provided according to an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of a rapid calibration scheme for multiple lidar systems according to an embodiment of this application;

[0046] Figure 3This is a block diagram of a rapid calibration device based on multi-solid-state lidar according to an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0048] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0049] The following description, with reference to the accompanying drawings, describes a rapid calibration method, apparatus, device, and medium based on multiple solid-state lidars according to embodiments of the present invention. Addressing the problem mentioned in the background art where manual calibration or automatic calibration involves manual parameter adjustment, leading to increased calibration complexity and reduced calibration efficiency and accuracy, this application provides a rapid calibration method based on multiple solid-state lidars. In this method, calibration instructions from multiple solid-state lidars are received; first point cloud data from the main lidar and second point cloud data from at least one slave lidar are acquired; a projection matrix from at least one slave lidar to the main lidar is obtained; and third point cloud data from at least one auxiliary lidar is acquired. A dense point cloud map is obtained based on the first, second, and third point cloud data; the projection is corrected based on the dense point cloud map; and the multiple solid-state lidars are then precisely calibrated using the corrected projection matrix. This solves the problem of increased calibration complexity and reduced efficiency and accuracy caused by manual calibration or automatic calibration involving manual parameter adjustment. Through a joint calibration process of coarse and fine calibration of multiple lidars, the calibration accuracy and unmanned operation efficiency of the multiple solid-state lidar are improved.

[0050] Figure 1 This is a flowchart illustrating a rapid calibration method based on multiple solid-state lidar according to an embodiment of the present invention.

[0051] like Figure 1 As shown, this rapid calibration method based on multi-solid-state lidar includes the following steps:

[0052] In step S101, the calibration command of the multi-solid-state lidar is received.

[0053] Specifically, in this embodiment of the application, if it is necessary to calibrate the target point cloud scene, a calibration command is first sent to the multi-solid-state LiDAR to calibrate the target point cloud scene according to the calibration command of the multi-solid-state LiDAR.

[0054] In step S102, based on the calibration instructions, the multi-solid-state lidar is divided into a master lidar and at least one slave lidar, and the first point cloud data of the master lidar and the second point cloud data of at least one slave lidar are acquired. The projection matrix from at least one slave lidar to the master lidar is obtained based on the first point cloud data and the second point cloud data.

[0055] Furthermore, in some embodiments, acquiring first point cloud data of the main lidar and second point cloud data of at least one slave lidar, and obtaining a projection matrix from at least one slave lidar to the main lidar based on the first and second point cloud data, includes: acquiring target field-of-view feature points of the main lidar and at least one slave lidar based on the first and second point cloud data; iteratively solving the target field-of-view feature points based on a preset matching algorithm, and outputting a projection matrix from at least one slave lidar to the main lidar, so as to coarsely calibrate the multiple solid-state lidars based on the projection matrix.

[0056] Furthermore, in some embodiments, obtaining target field-of-view feature points of the main lidar and at least one slave lidar includes: obtaining target feature points and target feature lines of the target point cloud scene; and obtaining target field-of-view feature points of the main lidar and at least one slave lidar based on the target feature points and target feature lines.

[0057] The preset matching algorithm can be a relevant matching algorithm adopted by those skilled in the art based on actual calibration requirements, or it can be a matching algorithm obtained through multiple computer simulations. No specific limitation is made here.

[0058] Specifically, such as Figure 2 As shown, the multi-solid-state lidar in this embodiment can be divided into a main lidar, at least one slave lidar, and auxiliary lidars. The main lidar serves as the coordinate system reference center for the entire multi-solid-state lidar system. The at least one slave lidar refers to the other lidars in the multi-solid-state lidar system besides the main lidar. The auxiliary lidars are used for auxiliary calibration.

[0059] Specifically, in the calibration of the target point cloud scene in this application embodiment, firstly, within the common target point cloud scene of the main lidar and at least one slave lidar, the main lidar and at least one slave lidar respectively acquire target feature points and target feature lines of the target point cloud scene, and perform ICP (Iterative Closest Point) matching on the target feature points and target feature lines acquired by the main lidar and at least one slave lidar, such as static obstacles and static backgrounds like lampposts, trees, stop lines, stationary vehicles, etc., and calibrate the acquired target feature points and target feature lines to the main lidar, thereby obtaining the target field-of-view feature points of the main lidar and at least one slave lidar; secondly, based on a preset matching algorithm, such as the ICP algorithm, the target field-of-view feature points are iteratively solved, thereby outputting the projection matrix of at least one slave lidar to the main lidar, so as to coarsely calibrate the multiple solid-state lidars according to the projection matrix.

[0060] It should be noted that since the common target point cloud scene of at least one slave lidar and the master lidar is relatively small and the distance is relatively far, the embodiments of this application can use at least one adjacent slave lidar for pairwise calibration, and then iterate the calibration results to the master lidar for calibration, and finally output the projection matrix of at least one slave lidar to the master lidar, so as to coarsely calibrate the multiple solid-state lidars according to the projection matrix.

[0061] In step S103, third point cloud data of at least one auxiliary radar is acquired, and a dense point cloud map is obtained based on the first point cloud data, the second point cloud data and the third point cloud data. The projection matrix is ​​then corrected based on the dense point cloud map so as to accurately calibrate the multi-solid-state lidar based on the corrected projection matrix.

[0062] Specifically, in order to improve the calibration accuracy, after coarsely calibrating the target point cloud scene, this application embodiment needs to construct an offline point cloud map through a point cloud mapping module and use the SLAM (Simultaneous Localization and Mapping) algorithm to realize the fine point cloud map construction of the entire target point cloud scene, so as to improve the accuracy of multi-solid-state LiDAR calibration.

[0063] Specifically, the embodiments of this application require the use of at least one auxiliary lidar to construct a dense point cloud map of the common target point cloud scene of the main lidar and at least one slave lidar. That is, a dense point cloud map is obtained based on the first point cloud data of the main lidar, the second point cloud data of at least one slave lidar, and the third point cloud data of at least one auxiliary lidar. After obtaining the dense point cloud map, the projection matrix is ​​corrected based on the dense point cloud map, so as to accurately calibrate the multi-solid-state lidars based on the corrected projection matrix.

[0064] Furthermore, in some embodiments, the projection matrix is ​​corrected based on the dense point cloud map to finely calibrate the multi-solid-state lidar based on the corrected projection matrix, including: matching the coarse calibration result of the multi-solid-state lidar with the dense point cloud map based on the dense point cloud map; and correcting the projection matrix through the fine calibration module based on the matching result to finely calibrate the multi-solid-state lidar based on the corrected projection matrix.

[0065] Furthermore, in some embodiments, the projection matrix is ​​corrected by a fine calibration module based on the matching result, so as to finely calibrate the multi-solid-state lidar based on the corrected projection matrix. This includes: performing coordinate transformation on the second point cloud data of at least one lidar to obtain the coordinate transformation result of the second point cloud data; completing visualization in the same coordinate system based on the transformation result and the first point cloud data, and determining whether the first point cloud data in the dense point cloud map matches the transformation result. If they do not match, the projection matrix of at least one lidar is corrected.

[0066] Specifically, in the process of correcting the projection matrix in this embodiment, it is necessary to match the calibration results of at least one LiDAR with the dense point cloud map. That is, the second point cloud data of at least one LiDAR is transformed by a coordinate system rotation and translation matrix to obtain the coordinate transformation result of the second point cloud data. Based on the transformation result and the first point cloud data, visualization is completed in the same coordinate system. This allows it to determine whether the first point cloud data in the dense point cloud map matches the transformation result, i.e., whether the transformation result matches the static background and static obstacles in the dense point cloud map. If they match, it indicates that the target point cloud scene calibration is complete. If they do not match, the projection matrix of at least one LiDAR is corrected by a fine calibration module. Based on the corrected projection matrix, the multiple solid-state LiDARs are finely calibrated, thereby reducing the error between the projections of at least one LiDAR and improving the accuracy of the multi-solid-state LiDAR calibration.

[0067] According to the rapid calibration method based on multiple solid-state lidars in this application, a calibration command for multiple solid-state lidars is received. First point cloud data from the main lidar and second point cloud data from at least one slave lidar are acquired to obtain a projection matrix from at least one slave lidar to the main lidar. Simultaneously, third point cloud data from at least one auxiliary lidar are acquired. A dense point cloud map is obtained based on the first, second, and third point cloud data. The projection is then corrected based on the dense point cloud map to precisely calibrate the multiple solid-state lidars using the corrected projection matrix. This solves the problem of complex calibration processes caused by manual calibration or manual parameter adjustment during automatic calibration, which reduces calibration efficiency and accuracy. By combining coarse and fine calibration of multiple lidars, the calibration accuracy and unmanned efficiency of the multiple solid-state lidars are improved.

[0068] Next, a block diagram of a rapid calibration device based on multi-solid-state lidar according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0069] like Figure 3 As shown, the rapid calibration device 10 based on multi-solid-state lidar includes: a receiving module 100, an acquisition module 200, and a correction module 300.

[0070] The receiving module 100 is used to receive calibration instructions from the multi-solid-state lidar.

[0071] The acquisition module 200 is used to, based on calibration instructions, divide multiple solid-state lidars into a master lidar and at least one slave lidar, acquire first point cloud data from the master lidar and second point cloud data from at least one slave lidar, and obtain the projection matrix from at least one slave lidar to the master lidar based on the first and second point cloud data; and

[0072] The correction module 300 is used to acquire third point cloud data of at least one auxiliary radar, obtain a dense point cloud map based on the first point cloud data, the second point cloud data and the third point cloud data, and correct the projection based on the dense point cloud map so as to accurately calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0073] Furthermore, in some embodiments, the acquisition module 200 is specifically used for:

[0074] Based on the first point cloud data and the second point cloud data, obtain the target field-of-view feature points of the main lidar and at least one slave lidar.

[0075] Based on a preset matching algorithm, the target field feature points are iteratively solved, and at least one projection matrix from the lidar to the main lidar is output, so as to coarsely calibrate the multi-solid-state lidar according to the projection matrix.

[0076] Furthermore, in some embodiments, the acquisition module 200 is specifically used for:

[0077] Obtain the target feature points and target feature lines of the target point cloud scene;

[0078] The target field-of-view feature points of the main lidar and at least one slave lidar are obtained based on the target feature points and target feature lines.

[0079] Furthermore, in some embodiments, the correction module 300 is specifically used for:

[0080] Based on the dense point cloud map, the coarse calibration results of the multi-solid-state lidar are matched with the dense point cloud map.

[0081] Based on the matching results, the projection matrix is ​​corrected by the fine calibration module to finely calibrate the multi-solid-state lidar according to the corrected projection matrix.

[0082] Furthermore, in some embodiments, the correction module 300 is specifically used for:

[0083] At least one coordinate transformation is performed on the second point cloud data from the lidar to obtain the coordinate transformation result of the second point cloud data;

[0084] Based on the transformation results and the first point cloud data, visualization is completed in the same coordinate system. It is determined whether the first point cloud data in the dense point cloud map matches the transformation results. If they do not match, at least one projection matrix from the LiDAR is corrected.

[0085] According to an embodiment of this application, a rapid calibration device based on multiple solid-state lidars receives calibration instructions from multiple solid-state lidars, acquires first point cloud data from the main lidar and second point cloud data from at least one slave lidar, obtains a projection matrix from at least one slave lidar to the main lidar, and simultaneously acquires third point cloud data from at least one auxiliary lidar. A dense point cloud map is obtained based on the first, second, and third point cloud data, and the projection is corrected based on the dense point cloud map to precisely calibrate the multiple solid-state lidars using the corrected projection matrix. This solves the problem of complex calibration processes caused by manual calibration or manual parameter adjustment during automatic calibration, which reduces calibration efficiency and accuracy. By combining coarse and fine calibration of multiple lidars, the calibration accuracy and unmanned efficiency of the multiple solid-state lidars are improved.

[0086] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0087] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0088] When the processor 402 executes the program, it implements the fast calibration method based on multi-solid-state lidar provided in the above embodiments.

[0089] Furthermore, electronic devices also include:

[0090] Communication interface 403 is used for communication between memory 401 and processor 402.

[0091] The memory 401 is used to store computer programs that can run on the processor 402.

[0092] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0093] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0094] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0095] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0096] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described rapid calibration method based on multi-solid-state lidar.

[0097] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0098] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0099] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0100] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A rapid calibration method based on multi-solid-state lidar, characterized in that, Includes the following steps: Receive calibration commands from multiple solid-state lidar systems; Based on the calibration instructions, the multi-solid-state lidar is divided into a master lidar and at least one slave lidar, and the first point cloud data of the master lidar and the second point cloud data of the at least one slave lidar are acquired. The projection matrix of the at least one slave lidar to the master lidar is obtained based on the first point cloud data and the second point cloud data. as well as Acquire third point cloud data from at least one auxiliary radar, and obtain a dense point cloud map based on the first point cloud data, the second point cloud data, and the third point cloud data. Then, correct the projection based on the dense point cloud map to precisely calibrate the multi-solid-state lidar based on the corrected projection matrix. The process of acquiring first point cloud data of the main lidar and second point cloud data of at least one slave lidar, and obtaining the projection matrix from the at least one slave lidar to the main lidar based on the first and second point cloud data, includes: acquiring target field-of-view feature points of the main lidar and the at least one slave lidar based on the first and second point cloud data; iteratively solving the target field-of-view feature points based on a preset matching algorithm, and outputting the projection matrix from the at least one slave lidar to the main lidar, so as to coarsely calibrate the multi-solid-state lidar based on the projection matrix; The step of obtaining the target field-of-view feature points of the main lidar and the at least one slave lidar includes: obtaining target feature points and target feature lines of the target point cloud scene; and obtaining the target field-of-view feature points of the main lidar and the at least one slave lidar based on the target feature points and the target feature lines. The projection matrix is ​​corrected based on the dense point cloud map to perform fine calibration of the multi-solid-state lidar based on the corrected projection matrix. This includes: matching the coarse calibration result of the multi-solid-state lidar with the dense point cloud map based on the dense point cloud map; and correcting the projection matrix through a fine calibration module based on the matching result to perform fine calibration of the multi-solid-state lidar based on the corrected projection matrix.

2. The method according to claim 1, characterized in that, Based on the matching result, the projection matrix is ​​corrected by the fine calibration module to perform fine calibration of the multi-solid-state lidar according to the corrected projection matrix, including: The coordinate transformation of the second point cloud data from the lidar is performed to obtain the coordinate transformation result of the second point cloud data; Visualization is performed in the same coordinate system based on the transformation result and the first point cloud data. It is determined whether the first point cloud data in the dense point cloud map matches the transformation result. If they do not match, the projection matrix of at least one lidar is corrected.

3. A rapid calibration device based on multi-solid-state lidar, characterized in that, include: The receiving module is used to receive calibration commands from multiple solid-state lidar systems. The acquisition module is used to divide the multi-solid-state lidar into a master lidar and at least one slave lidar based on the calibration command, and acquire the first point cloud data of the master lidar and the second point cloud data of the at least one slave lidar, and obtain the projection matrix of the at least one slave lidar to the master lidar based on the first point cloud data and the second point cloud data. as well as The correction module is used to acquire third point cloud data of at least one auxiliary radar, obtain a dense point cloud map based on the first point cloud data, the second point cloud data and the third point cloud data, and correct the projection based on the dense point cloud map, so as to accurately calibrate the multi-solid-state lidar based on the corrected projection matrix. Specifically, the acquisition module is used to: acquire target field-of-view feature points of the main lidar and the at least one slave lidar based on the first point cloud data and the second point cloud data; The target field feature points are iteratively solved based on a preset matching algorithm, and the projection matrix of at least one laser radar from the main laser radar is output so as to coarsely calibrate the multi-solid-state laser radar according to the projection matrix. The acquisition module is specifically used to: acquire target feature points and target feature lines of the target point cloud scene; and obtain target field-of-view feature points of the main lidar and the at least one slave lidar based on the target feature points and target feature lines. The correction module is specifically used to: match the coarse calibration result of the multi-solid-state lidar with the dense point cloud map based on the dense point cloud map; and correct the projection matrix through the fine calibration module according to the matching result, so as to finely calibrate the multi-solid-state lidar according to the corrected projection matrix.

4. The apparatus according to claim 3, characterized in that, The correction module is specifically used for: The coordinate transformation of the second point cloud data from the lidar is performed to obtain the coordinate transformation result of the second point cloud data; Visualization is performed in the same coordinate system based on the transformation result and the first point cloud data. It is determined whether the first point cloud data in the dense point cloud map matches the transformation result. If they do not match, the projection matrix of at least one lidar is corrected.

5. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the rapid calibration method based on multi-solid-state lidar as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the rapid calibration method based on multi-solid-state lidar as described in any one of claims 1-2.

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