Batch analysis method and system based on multidirectional attitude angles and electronic equipment

Abstract

The invention relates to a multidirectional attitude angle-based batch analysis method, which comprises the following steps of: S1, acquiring quaternions of laser radars in all directions on a vehicle in batches, and converting the quaternions into attitude angles of the laser radars in batches; s2, performing difference processing on the attitude angle and an initial attitude angle when the corresponding laser radar leaves a factory so as to obtain a deviation angle of each attitude and generate an attitude curve; and S3, analyzing the angle change of each laser radar on the vehicle in batches according to the curve. The method has the beneficial effects that the batch analysis efficiency is high: based on the multi-azimuth laser radar attitude angle curve, the limitation of single-equipment and single-dimension analysis is broken through, the batch parallel processing of multi-vehicle / multi-laser radar angle data is realized, and the analysis efficiency is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted sensing technology, specifically to a batch analysis method, system, and electronic device based on multi-directional attitude angles. Background Technology

[0002] In fields such as autonomous driving and intelligent connected vehicles, to achieve 360° all-around environmental perception, vehicles typically deploy multiple LiDARs in the front, rear, left, and right directions to work together. The accuracy of the LiDAR's pitch, roll, and yaw angles directly affects the accuracy of environmental perception. Factors such as vibration during vehicle operation, installation errors, and temperature changes can cause LiDAR angle deviations, thus requiring regular angle analysis and calibration of multiple LiDARs.

[0003] Existing vehicle lidar angle analysis methods have the following drawbacks: First, they mostly employ a method of calibrating each device individually. When the number of lidars is ≥4, the batch processing efficiency is extremely low, failing to meet the rapid response requirements of vehicle maintenance. Second, they rely solely on the angle data of a single lidar for analysis, failing to utilize the attitude angle correlation characteristics between multiple lidars, resulting in insufficient accuracy in angle deviation calculation and poor angle consistency among multiple devices after calibration. Third, traditional analysis methods have weak noise suppression and feature extraction capabilities for attitude angle curves, making it difficult to distinguish between effective angle offsets and curve fluctuations caused by environmental interference, thus affecting calibration reliability.

[0004] Therefore, a new method is urgently needed to achieve efficient tracking of large numbers of vehicles from multiple lidar angles. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing a batch analysis method, system, and electronic device based on multi-azimuth attitude angles.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: The first aspect of this invention protects a batch analysis method based on multi-azimuth attitude angles, comprising: S1. Batch collect quaternions of LiDARs in each direction on the vehicle and convert them into LiDAR attitude angles in batches; S2. The attitude angle is subtracted from the initial attitude angle of the corresponding lidar at the time of manufacture to obtain the deviation angle of each attitude and generate an attitude curve; S3. Analyze the angle changes of each lidar on the vehicle in batches based on the curves.

[0007] As a further technical solution, in step S1, the quaternion of the lidar is collected by the inertial measurement unit integrated with the lidar, and the quaternion is filtered and optimized to be converted into the attitude angle of the lidar.

[0008] As a further technical solution, the attitude angles include roll angle, pitch angle and yaw angle.

[0009] As a further technical solution, in step S2, the generated deviation angles include the pitch deviation angles of the lidar in the front and rear directions of each vehicle, the roll deviation angles of the lidar in the left and right directions of each vehicle, and the yaw deviation angles of the lidar in the front, rear, left, and right directions of each vehicle.

[0010] As a further technical solution, in step S2, curves are generated for the pitch deviation angle of the lidar in the front and rear positions and the roll deviation angle of the lidar in the left and right positions of different vehicles. The yaw deviation angles of the lidar in the four directions of the vehicle (front, rear, left, and right) are generated as curves.

[0011] As a further technical solution, in step S3, observe whether the curve generated by the pitch deviation angle of the front lidar is consistent with the curve generated by the roll deviation angle of the left lidar, and whether the curve generated by the pitch deviation angle of the rear lidar is consistent with the curve generated by the roll deviation angle of the right lidar, so as to batch identify vehicles with offset lidar attitude angles. If the curves do not follow the same trend, compare whether the pitch deviation angles of the front and rear lidars on the same vehicle are symmetrical, whether the roll deviation angles of the left and right lidars are symmetrical, and whether the yaw deviation angles of the lidars in the four directions (front, rear, left, and right) follow the same trend, so as to analyze whether the attitude angles of each lidar on the vehicle need to be corrected.

[0012] A second aspect of this invention protects a batch analysis system based on multi-azimuth attitude angles, the system comprising: First processing module: Batch collection of quaternions from LiDARs in all directions on the vehicle, and batch conversion of them into LiDAR attitude angles; The second processing module: calculates the difference between the attitude angle and the initial attitude angle of the corresponding lidar when it leaves the factory to obtain the deviation angle of each attitude and generate an attitude curve; The third processing module analyzes the angle changes of each lidar on the vehicle in batches based on the curves.

[0013] The third aspect of this invention protects an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the batch analysis method based on multi-azimuth attitude angles as described in the first aspect.

[0014] The beneficial effects of this invention are: 1. High efficiency in batch analysis: Based on the attitude angle curves of multi-directional LiDAR, it breaks through the limitations of single-device and single-dimensional analysis, realizes batch parallel processing of angle data of multiple vehicles / multiple LiDARs, and greatly improves analysis efficiency; 2. Quaternion conversion of lidar attitude angles is adopted to avoid the gimbal lock problem of traditional Euler angles, thereby improving the accuracy and stability of attitude angle calculation; 3. Integrate the attitude angle curve features of multi-directional LiDAR to cover the angle change monitoring needs in all vehicle driving scenarios; 4. High adaptability: It can adapt to different vehicle driving conditions and multiple LiDAR configurations, and can continuously monitor LiDAR angle changes on a large scale. It is suitable for scenarios that require large-scale, high-precision dynamic angle tracking, such as autonomous driving, UAV environmental perception, and industrial automation inspection. Attached Figure Description

[0015] Figure 1 This is a flowchart of the batch analysis method based on multi-azimuth attitude angles in Embodiment 1. Figure 2 This is a graph showing the yaw deviation angle of the lidar in each direction on 20 vehicles in Embodiment 1 of the present invention; Figure 3 The diagram shows the pitch deviation angle curves of the front and rear lidars and the roll deviation angle curves of the left and right lidars on 20 vehicles in Embodiment 1 of the present invention. Figure 4 This is a structural block diagram of a batch analysis system based on multi-azimuth attitude angles, as shown in Example 2. Figure 5 This is a structural block diagram of an electronic device according to Embodiment 3. Detailed Implementation

[0016] 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, and 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] In the description of this application, 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 indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0018] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0019] Example 1 This embodiment simultaneously collects data from lidar sensors on multiple vehicles in various directions to achieve batch analysis of the angular accuracy of lidar sensors on each vehicle, facilitating subsequent calibration.

[0020] See Figure 1 This embodiment provides a batch analysis method based on multi-azimuth attitude angles, including: S1. Batch collect quaternions of LiDARs in each direction on the vehicle and convert them into LiDAR attitude angles in batches; S2. The attitude angle is subtracted from the initial attitude angle of the corresponding lidar at the time of manufacture to obtain the deviation angle of each attitude and generate an attitude curve; S3. Analyze the angle changes of each lidar on the vehicle in batches based on the curves.

[0021] It should be noted that in step S1, the quaternion of the lidar is collected by the inertial measurement unit integrated with the lidar, and the quaternion is filtered and optimized to be converted into the attitude angle of the lidar.

[0022] The attitude angles include roll angle, pitch angle, and yaw angle. For example, in a vehicle-mounted scenario, the default vehicle coordinate system is (X-axis forward, Y-axis left, Z-axis up, based on the right-hand rule). When the LiDAR's roll angle around the forward direction is the roll angle, the rotation axis is the X-axis of the vehicle coordinate system; when the LiDAR's tilt angle around the lateral direction is the pitch angle, the rotation axis is the Y-axis of the vehicle coordinate system; when the LiDAR's yaw angle around the vertical direction is the yaw angle, the rotation axis is the Z-axis of the vehicle coordinate system. The roll angle ranges from -180° to 180°, the pitch angle ranges from -90° to 90°, and the yaw angle ranges from -180° to 180° to ensure complete coverage of all attitude changes during vehicle movement (such as cornering roll, acceleration pitch, braking pitch, etc.).

[0023] In the specific implementation process: Kalman filtering algorithm is used to suppress noise in the quaternions, removing quaternion fluctuations caused by environmental interference and sensor errors; then, based on the right-hand coordinate system, the filtered quaternions are converted into lidar attitude angles, namely roll, pitch, and yaw, using preset conversion formulas, as shown below: Roll = arctan2(2(q0q1+ q2q3), 1 - 2(q1² + q2²)) Pitch = arcsin(2(q0q2- q3q1)) Yaw = arctan2(2(q0q3+ q1q2), 1 - 2(q2² + q3²)) Where q0 is the real part of the quaternion, and q1, q2, and q3 are the imaginary parts of the quaternion.

[0024] In step S2, the generated deviation angles include the pitch deviation angles of the lidar in the front and rear positions of each vehicle, the roll deviation angles of the lidar in the left and right positions of each vehicle, and the yaw deviation angles of the lidar in the front, rear, left, and right positions of each vehicle. Subsequently, curves can be generated for the pitch deviation angles of the lidar in the front and rear positions and the roll deviation angles of the lidar in the left and right positions of different vehicles; curves can also be generated for the yaw deviation angles of the lidar in the front, rear, left, and right positions of each vehicle.

[0025] For example, in this process, the first-order difference method is used to calculate the angle difference between adjacent sampling points of the curve. When the difference exceeds 0.5°, it is determined to be a sudden change. The time of the sudden change, the magnitude of the sudden change, and the duration are recorded. Invalid sudden changes with a duration ≤ 5 sampling periods are removed.

[0026] In step S3, observe whether the curve generated by the pitch deviation angle of the front lidar is consistent with the curve generated by the roll deviation angle of the left lidar, and whether the curve generated by the pitch deviation angle of the rear lidar is consistent with the curve generated by the roll deviation angle of the right lidar, so as to batch identify vehicles with lidar attitude angle deviation. If the curve trends are inconsistent, compare the points on the vehicle corresponding to the abnormal points in the curve: whether the pitch deviation angles of the front and rear lidars are symmetrical, whether the roll deviation angles of the left and right lidars are symmetrical, and whether the yaw deviation angles of the lidars in the four directions (front, rear, left, and right) follow the same trend, in order to analyze whether the attitude angles of each lidar on the vehicle need to be corrected.

[0027] That is, first observe whether the trend of the curve is consistent to determine whether the LiDAR angle of a number of vehicles being tested has shifted. Then, select the vehicles with attitude angle shifts and compare whether the pitch deviation angles of the front and rear LiDARs are symmetrical, whether the roll deviation angles of the left and right LiDARs are symmetrical, and whether the yaw deviation angles of the LiDARs in the four directions (front, rear, left, and right) follow the same trend. This allows for an accurate and quick understanding from the curve which attitude angle(s) or roll angles of the corresponding LiDARs in a specific vehicle need to be corrected.

[0028] For example, if the pitch deviation angles of the front and rear lidars on the same vehicle are not asymmetrical, the angle deviation is calculated and the Euler angles (such as roll angle, pitch angle, and yaw angle) on the corresponding vehicle are corrected. Then, based on the calibrated Euler angles and the scanning frequency of the lidar, the current angle information of the lidar is updated in real time to complete the angle tracking.

[0029] For example, there are 20 vehicles, each numbered AT. The goal is to obtain the deviation angle (such as pitch deviation angle, roll deviation angle, and yaw deviation angle) data of the lidar in the four directions of front, rear, left, and right on the 20 vehicles, as shown in Table 1 below: Table 1 Attitude angles of various lidar sensors on the A-R vehicle

[0030] Based on the data in Table 1 above, a curve can be plotted, and a linear fit can be performed on the yaw deviation angle curve to obtain an overlapping trend line. When there are obvious inconsistencies in the trend line corresponding to vehicles, the angle and vehicle inspection need to be re-verified (see...). Figure 2 ); from Figure 2 As can be seen, on the first vehicle, the yaw deviation angle of the right lidar is significantly inconsistent with the trends of the yaw deviation angles of the lidars in the front, rear, and left directions; on the fifteenth vehicle, the yaw deviation angle of the rear lidar is significantly inconsistent with the trends of the yaw deviation angles of the lidars in the front, left, and right directions. Therefore, it is necessary to re-verify and correct the yaw angle of the right lidar on the first vehicle and the yaw angle of the rear lidar on the fifteenth vehicle.

[0031] For example, using a 500ms window, calculate the cross-correlation coefficients between the corresponding attitude angle curves of the lidar in the four azimuths (such as the pitch deviation angle curves of the front and rear lidars and the roll deviation angle curves of the left and right lidars) in the front, rear, left, and right directions. Figure 3 This reflects the synergy of angle changes among multiple lidar sensors, with a preset cross-correlation coefficient ≥ 0.9.

[0032] according to Figure 3As shown, the pitch deviation angle curves of the front and rear lidars of the same vehicle are symmetrical, and the roll deviation angle curves of the left and right lidars are symmetrical. The overall symmetry of the curves indicates that there are no lidar angle variation issues in this batch of vehicles. When the curves show obvious asymmetry and low overlap, it is necessary to check the vehicles at the corresponding points to determine whether the lidar angles of a large number of vehicles are normal.

[0033] This embodiment can simultaneously collect attitude angle data from multiple LiDARs on multiple vehicles, enabling synchronous analysis and batch processing of the angles of multiple LiDARs. During this process, quaternion-based conversion of LiDAR Euler angles is used to avoid the gimbaling problem inherent in traditional Euler angles, improving the accuracy and stability of attitude angle calculation. The angle deviation calculation error is ≤0.03°, effectively distinguishing between angle offset and environmental interference, significantly improving calibration reliability. Furthermore, by subtracting the obtained attitude angles from the corresponding LiDAR's initial attitude angle at the factory, the deviation angle of each attitude is obtained and attitude curves are generated. This overcomes the limitations of single-device, single-dimensional analysis, allowing batch observation of angle changes on vehicle LiDARs, solving the technical problems of low efficiency and poor consistency in batch angle analysis; and covering the angle change monitoring needs across all vehicle driving scenarios.

[0034] Example 2 This embodiment provides a batch analysis system based on multi-azimuth attitude angles, the system comprising: First processing module: Batch collection of quaternions from LiDARs in all directions on the vehicle, and batch conversion of them into LiDAR attitude angles; Specifically, the quaternions of the lidar are collected by the inertial measurement unit integrated with the lidar, and the quaternions are filtered and optimized to be converted into the attitude angles of the lidar; and the attitude angles include roll angle, pitch angle and yaw angle.

[0035] The second processing module: calculates the difference between the attitude angle and the initial attitude angle of the corresponding lidar when it leaves the factory to obtain the deviation angle of each attitude and generate an attitude curve; Specifically, the generated deviation angles include the pitch deviation angles of the lidar in the front and rear positions of each vehicle, the roll deviation angles of the lidar in the left and right positions of each vehicle, and the yaw deviation angles of the lidar in the front, rear, left, and right positions of each vehicle; curves are generated for the pitch deviation angles of the lidar in the front and rear positions and the roll deviation angles of the lidar in the left and right positions of different vehicles. The yaw deviation angles of the lidar in the four directions of the vehicle (front, rear, left, and right) are generated as curves.

[0036] The third processing module analyzes the angle changes of each lidar on the vehicle in batches based on the curves. Specifically, observe whether the curves generated by the pitch deviation angle of the front lidar and the roll deviation angle of the left lidar are consistent, and whether the curves generated by the pitch deviation angle of the rear lidar and the roll deviation angle of the right lidar are consistent, in order to identify vehicles with offset lidar attitude angles in batches. If the curve trends are inconsistent, compare the points on the vehicle corresponding to the abnormal points in the curves: whether the pitch deviation angles of the front and rear lidars are symmetrical, whether the roll deviation angles of the left and right lidars are symmetrical, and whether the yaw deviation angles of the lidars in the four directions (front, rear, left, and right) follow the same trend, in order to analyze whether the attitude angles of each lidar on the vehicle need to be corrected.

[0037] Example 3 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the batch analysis method based on multi-azimuth attitude angles as described in Embodiment 1.

[0038] See Figure 5 The present invention describes a structural block diagram of an electronic device 100 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0039] Electronic device 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in read-only memory (ROM) 102 or a computer program loaded from storage unit 108 into random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of electronic device 100. The computing unit 101, ROM 102, and RAM 103 are interconnected via bus 104. Input / output (I / O) interface 605 is also connected to bus 104.

[0040] Multiple components in electronic device 100 are connected to I / O interface 605, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information to electronic device 100. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 107 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, a hard disk and an optical disk. Communication unit 109 allows electronic device 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0041] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above, such as image processing methods. For example, in some embodiments, the image processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 100 via ROM 102 and / or communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the image processing method described above may be performed. Alternatively, in other embodiments, the computing unit 101 may be configured to perform image processing methods by any other suitable means (e.g., by means of firmware).

[0042] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0043] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0044] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, and portable compact disc read-only memory (CD). ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0045] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: 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, 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).

[0046] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0047] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via a communication network. This is achieved by having clients running on corresponding computers and interacting with each other. Computer programs that establish server-side relationships use this information to create client-server relationships. A server can be a cloud server, a server in a distributed system, or a server integrated with blockchain technology.

[0048] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0049] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A batch analysis method based on multi-azimuth attitude angles, characterized in that, include: S1. Batch collect quaternions of LiDARs in each direction on the vehicle and convert them into LiDAR attitude angles in batches; S2. The attitude angle is subtracted from the initial attitude angle of the corresponding lidar at the time of manufacture to obtain the deviation angle of each attitude and generate an attitude curve; S3. Analyze the angle changes of each lidar on the vehicle in batches based on the curves.

2. The batch analysis method based on multi-azimuth attitude angles according to claim 1, characterized in that, In step S1, the quaternion of the lidar is collected by the inertial measurement unit integrated with the lidar, and the quaternion is filtered and optimized to be converted into the attitude angle of the lidar.

3. The batch analysis method based on multi-azimuth attitude angles according to claim 2, characterized in that, The attitude angles include roll angle, pitch angle, and yaw angle.

4. The batch analysis method based on multi-azimuth attitude angles according to claim 1, characterized in that, In step S2, the generated deviation angles include the pitch deviation angles of the lidar in the front and rear positions of each vehicle, the roll deviation angles of the lidar in the left and right positions of each vehicle, and the yaw deviation angles of the lidar in the front, rear, left, and right positions of each vehicle.

5. The batch analysis method based on multi-azimuth attitude angles according to claim 4, characterized in that, In step S2, curves are generated for the pitch deviation angle of the lidar in the front and rear positions and the roll deviation angle of the lidar in the left and right positions of different vehicles. The yaw deviation angles of the lidar in the four directions of the vehicle (front, rear, left, and right) are generated as curves.

6. The batch analysis method based on multi-azimuth attitude angles according to claim 5, characterized in that, In step S3, observe whether the curve generated by the pitch deviation angle of the front lidar is consistent with the curve generated by the roll deviation angle of the left lidar, and whether the curve generated by the pitch deviation angle of the rear lidar is consistent with the curve generated by the roll deviation angle of the right lidar, so as to identify vehicles with offset lidar attitude angles in batches. If the curve trends are inconsistent, compare the points on the vehicle corresponding to the abnormal points in the curves: whether the pitch deviation angles of the front and rear lidars are symmetrical, whether the roll deviation angles of the left and right lidars are symmetrical, and whether the yaw deviation angles of the lidars in the four directions (front, rear, left, and right) follow the same trend, in order to analyze whether the attitude angles of each lidar on the vehicle need to be corrected.

7. A batch analysis system based on multi-azimuth attitude angles, characterized in that: The system includes: First processing module: Batch collection of quaternions from LiDARs in all directions on the vehicle, and batch conversion of them into LiDAR attitude angles; The second processing module: calculates the difference between the attitude angle and the initial attitude angle of the corresponding lidar when it leaves the factory to obtain the deviation angle of each attitude and generate an attitude curve; The third processing module analyzes the angle changes of each lidar on the vehicle in batches based on the curves.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the batch analysis method based on multi-azimuth attitude angles as described in any one of claims 1-6.

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