Installation and adjustment device and method for laser radar

Abstract

The invention belongs to the technical field of radar installation and adjustment, and mainly relates to laser radar installation and adjustment equipment which comprises a laser radar, an adjustment frame and a control terminal. The laser radar is composed of a control module and a measurement module. The measurement module comprises a laser transmitter, a receiver and a processing unit. The control module is connected with the control terminal, calculates an error by comparing the position information, and outputs an adjustment amount by using a neural network unit; the control terminal provides adjustment information to train the neural network unit; the debugging frame is responsible for assembling the laser radar and adjusting the pose of the laser radar according to instructions of the control module. Through the design, the automation level and the accuracy of debugging are greatly improved, the dependence on manual operation is reduced, the debugging time is shortened, and the working efficiency is improved. In addition, the invention further designs a laser radar installation and adjustment method, and the technical defect that the laser radar cannot be adaptively installed and adjusted in the prior art is overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser radar debugging, and in particular relates to a laser radar installation and adjustment device and an installation and adjustment method. Background Art

[0002] As an advanced measurement technology, LiDAR has been widely used in many fields such as automobiles, unmanned driving, geographic surveying and mapping, construction engineering, and security monitoring. With the development of technologies such as autonomous driving, smart logistics, and smart transportation, the frequency of use of LiDAR continues to rise. Among them, the automotive industry regards LiDAR as a key component for achieving high-precision environmental perception. However, the debugging of LiDAR has become an important issue in its use. Because the performance of LiDAR directly depends on the accuracy of its position and posture, any small adjustment error will have a significant impact on its measurement results. Therefore, improving the debugging efficiency and accuracy of LiDAR has become a technical problem that needs to be solved urgently.

[0003] In the current LiDAR debugging process, manual operation is mainly relied on, and engineers often need to manually perform multiple measurements and adjustments. This manual debugging method is not only time-consuming and labor-intensive, but also requires extremely high professional knowledge and experience of the operator. In addition, although some technologies have achieved remote control, such as debugging assisted by software intervention, in any case, each adjustment still needs to rely on the direct operation and real-time monitoring of engineers, making it difficult to automate the debugging process, which in turn affects the rapid deployment and use efficiency of LiDAR.

[0004] Based on this, it is urgent to improve the existing laser radar installation and adjustment equipment and methods to solve the technical defects of the existing technology. Summary of the invention

[0005] One of the purposes of the present invention is to provide a laser radar assembly and adjustment device that can adaptively adjust the radar posture in order to address the deficiencies of the prior art.

[0006] In order to achieve the above-mentioned invention objectives, this application implements the following technical solutions:

[0007] A laser radar assembly and adjustment device, comprising a laser radar, a debugging frame and a control terminal communicating with the laser radar;

[0008] The laser radar includes a control module and a measurement module. The measurement module includes a laser transmitter and a laser receiver and a processing unit for processing information of the laser transmitter and the laser receiver. The laser transmitter is used to transmit laser to the object to be measured, and the laser receiver is used to receive the laser transmitted back by the object to be measured. The processing unit is used to output the position information of the object to be measured to the control module.

[0009] The control module is connected to the control terminal in communication, and the error parameter is obtained by comparing the position information of the measured object transmitted by the control terminal with the position information transmitted by the processing unit; the control module also includes a neural network unit, and the neural network unit outputs the adjustment amount of the laser radar to the debugging frame according to the error parameter;

[0010] The control terminal transmits adjustment information to the neural network unit, and the adjustment information is used to train the neural network unit to output a corresponding adjustment amount;

[0011] The debugging frame is used to assemble the laser radar and is electrically connected to the control module. The debugging frame adjusts the position and posture of the laser radar according to the adjustment amount transmitted by the neural network unit in the control module.

[0012] The above technical solution produces the following technical effects:

[0013] An adaptive laser radar adjustment device based on a neural network unit can effectively solve the shortcomings of existing debugging methods. By combining the control module of the laser radar with the measurement module, and obtaining the position information of the measured object in real time through the control terminal, accurate calculation of error parameters can be achieved. The neural network unit will process these error parameters and output the corresponding adjustment amount according to the received information, thereby automatically adjusting the position and posture of the laser radar. This method not only greatly improves the automation level and accuracy of debugging, but also reduces the dependence on manual operation, reduces debugging time, and improves work efficiency.

[0014] As a further improvement to a laser radar assembly and adjustment device of the present invention, the position information of the object under test output by the processing unit is three-dimensional information of at least one object under test; the position information of the object under test transmitted by the control terminal is also three-dimensional information of at least one object under test.

[0015] As a further improvement to a laser radar assembly and adjustment device of the present invention, the control module is used to compare the three-dimensional information of the object under test output by the processing unit with the three-dimensional information of the object under test transmitted by the control terminal to output at least one error parameter.

[0016] As a further improvement to a laser radar assembly and adjustment device of the present invention, the control module performs Euclidean distance calculation on the difference between the three-dimensional information of the object under test output by the processing unit and the three-dimensional information of the object under test transmitted by the control terminal to obtain an error parameter.

[0017] As a further improvement to the laser radar assembly and adjustment equipment of the present invention, the adjustment information transmitted by the control terminal includes at least one set of error information and an adjustment amount matching the error information.

[0018] As a further improvement to a laser radar assembly and adjustment device of the present invention, the neural network unit includes a training set and an additional set. The training set is used to store the adjustment information transmitted by the control terminal; the additional set is used to store the adjustment amount information and error information output by the neural network unit.

[0019] As a further improvement to a laser radar assembly and adjustment device of the present invention, the error information in the training set is represented by different categories of a designated first eigenvector, and the error information in the additional set is divided into different categories by a designated second eigenvector according to the similarity between the second eigenvector and the first eigenvector of the error information in the training set by the neural network unit.

[0020] As a further improvement to the laser radar assembly and adjustment device of the present invention, the neural network unit confirms the similarity between the second vector of the error information in at least one additional set and the first vector of the error information in any training set, and confirms the error information pointed to by the second vector with a similarity greater than a first threshold as the category expressed by the first vector of the error information in the training set;

[0021] The neural network unit uses the adjustment amount associated with the error information of the same type as the error information pointed to by the second vector in the additional set in the training set as the output of the neural network unit.

[0022] As a further improvement to the laser radar assembly and adjustment equipment of the present invention, the control module is electrically connected to the display.

[0023] The second purpose of the present invention is to provide a laser radar adjustment method to address the shortcomings of the prior art and to solve the technical defect that the prior art cannot adaptively adjust the laser radar.

[0024] In order to achieve the above-mentioned invention objectives, this application implements the following technical solutions:

[0025] A laser radar adjustment method, comprising any of the above laser radar adjustment equipment, the laser radar adjustment method comprises the following steps:

[0026] S101, start the laser radar installation and adjustment equipment to put the laser radar in a test state; input the position information of the object to be measured through the control terminal, start the laser radar to emit laser and receive the reflected signal;

[0027] S201, the processing unit processes and outputs the position information of the measured object to the control module; the control module compares the position information output by the processing unit with the position information input by the control terminal, and calculates the error parameter;

[0028] S301, the control module transmits the error parameter to the neural network unit, and the neural network unit outputs the adjustment amount to the debugging frame according to the error parameter; the debugging frame adjusts the position and posture of the laser radar according to the adjustment amount transmitted by the control module;

[0029] S401, the neural network unit records and analyzes the error parameters and adjustment amounts after each adjustment, which are used to optimize the neural network unit; after completing the installation and adjustment of the laser radar, the control module synchronously displays the final adjustment results and error parameters through the display.

[0030] The above technical solution produces the following technical effects:

[0031] The technical solution of this application introduces a neural network unit, which enables the laser radar to have adaptive capabilities during automatic adjustment, and realizes real-time adaptation and optimization under various environmental changes. This technological innovation effectively solves the defects in traditional debugging methods and promotes the wider application of laser radar equipment in various fields. By learning based on the received error information, the neural network unit can not only improve the intelligent level of debugging, but also gradually improve the self-learning ability of the system, making the debugging of the laser radar more intelligent and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0033] Figure 1 This is a flowchart of the embodiment 1 of the present invention;

[0034] Figure 2 This is a schematic diagram of the structure of Example 1 of the present invention;

[0035] Figure 3 Schematic diagram of the structure of the laser radar in Example 1 of the present invention;

[0036] Figure 4 This is a schematic diagram of the structure of the neural network unit in Example 2 of the present invention;

[0037] Figure 5 This is a working diagram of the neural network unit in Example 2 of the present invention;

[0038] in:

[0039] 1-LiDAR;

[0040] 11- control module;

[0041] 12- Measurement module;

[0042] 121-Laser transmitter;

[0043] 122- laser receiver;

[0044] 13- Processing unit;

[0045] 14-Neural network unit;

[0046] 141-training set;

[0047] 142-Additional set;

[0048] 15 - Display;

[0049] 2-Debugging rack;

[0050] 3- Control terminal. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0052] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the claims. Any technical personnel in this field may make several possible changes and modifications without departing from the concept of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0054] The present invention is further described in detail below in conjunction with specific embodiments, but the embodiments of the present invention are not limited thereto.

[0055] Example 1

[0056] like Figure 1-3As shown, the present application has made improvements to the existing laser radar 1 assembly and adjustment device in order to solve the technical defect that each adjustment of the laser radar 1 in the prior art needs to rely on the direct operation and real-time monitoring of engineers, which makes it difficult to automate the debugging process, thereby affecting the rapid deployment and use efficiency of the laser radar 1. Specifically, the laser radar 1 debugging device of the present application includes a laser radar 1, a debugging frame 2, and a control terminal 3 that is communicatively connected to the laser radar 1; further, the laser radar 1 includes a control module 11 and a measuring module 12, the measuring module 12 includes a laser transmitter 121 and a laser receiver 122, and a processing unit 13 that processes the information of the laser transmitter 121 and the laser receiver 122, the laser transmitter 121 is used to emit a laser to the object to be measured, the laser receiver 122 is used to receive the laser transmitted back by the object to be measured, and the processing unit 13 is used to output the position information of the object to be measured to the control module 11;

[0057] Furthermore, the control module 11 is connected to the control terminal 3 for communication, and the error parameter is obtained by comparing the position information of the measured object transmitted by the control terminal 3 with the position information transmitted by the processing unit 13; the control module 11 also includes a neural network unit 14, and the neural network unit 14 outputs the adjustment amount of the laser radar 1 to the debugging frame 2 according to the error parameter;

[0058] The control terminal 3 transmits adjustment information to the neural network unit 14, and the adjustment information is used to train the neural network unit 14 to output the corresponding adjustment amount; the debugging frame 2 is used to assemble the laser radar 1 and is connected to the control module 11, and the debugging frame 2 adjusts the posture of the laser radar 1 according to the adjustment amount transmitted by the control module 11.

[0059] Specifically, the working principle of the above technical solution is as follows: during the debugging process of the laser radar 1, first, the laser transmitter 121 emits a laser beam to the object to be measured, and the laser receiver 122 receives the reflected laser signal. The processing unit 13 processes these signals, calculates the position information of the object to be measured, and transmits it to the control module 11. The control module 11 compares the received position information with the reference position information provided by the control terminal 3 to determine the error parameter. Secondly, the neural network unit 14 receives the error parameter and outputs the parameters required to adjust the posture of the laser radar 1.

[0060] Specifically, the parameters required to adjust the posture of the laser radar 1 include the pitch angle (Pitch, θ) information, azimuth angle (Yaw, ψ) information and roll angle (Roll, φ) information of the laser radar 1. The pitch angle information is used to affect the up and down direction of the laser radar 1, the azimuth angle information is used to affect the left and right direction of the laser radar 1, and the roll angle (Roll, φ) information is used to affect the side turn direction of the laser radar 1. The debugging frame 2 adjusts the posture of the laser radar 1 according to these parameters to ensure that the laser beam accurately points to the object being measured. During the adjustment process, the neural network unit 14 records the error parameters and adjustment amount in real time, and improves the accuracy of the adjustment through continuous learning and optimization. Finally, when the posture of the laser radar 1 is adjusted to meet the preset accuracy requirements, the control module 11 displays the adjustment results and error parameters through the display 15 to complete the entire adjustment process. In this way, the adjustment efficiency and accuracy of the laser radar 1 are significantly improved, while reducing the dependence on professional engineers and reducing the debugging cost, providing strong technical support for the rapid deployment and wide application of the laser radar 1.

[0061] Furthermore, the control terminal 3 provides adjustment information by interacting with the neural network unit 14 (the adjustment information has been transmitted to the neural network unit 14 of the control module 11 before the laser radar 1 is working, and can be directly called from the neural network unit 14 during the use of the laser radar 1). This information is used to train the neural network unit 14 to optimize the adjustment amount of its output. The debugging frame 2 accurately adjusts the posture of the laser radar 1 according to the adjustment amount instruction issued by the control module 11 to reduce errors and improve measurement accuracy.

[0062] Through the above technical solution, the debugging process of the laser radar 1 can be automated, reducing the reliance on direct operations of engineers, thereby improving the rapid deployment capability and use efficiency of the laser radar 1. In addition, through the self-learning and adjustment of the neural network unit 14, the accuracy and efficiency of debugging can be further improved.

[0063] Furthermore, the position information of the measured object output by the processing unit 13 is three-dimensional information of at least one measured object; the position information of the measured object transmitted by the control terminal 3 is also three-dimensional information of at least one measured object. The three-dimensional information includes coordinate data of three dimensions: X, Y, and Z. The control module 11 compares these three-dimensional information and calculates the error parameter, which reflects the deviation between the posture of the laser radar 1 and the ideal posture. The neural network unit 14 uses these error parameters and outputs the corresponding adjustment amount through its internal algorithm to guide the debugging frame 2 to adjust the posture of the laser radar 1.

[0064] Furthermore, the control module 11 is used to compare the three-dimensional information of the object under test output by the processing unit 13 with the three-dimensional information of the object under test transmitted by the control terminal 3, and output at least one error parameter. The reason for the design here is that in the actual measurement process, a single object under test may contain multiple three-dimensional information, and these three-dimensional information contain multiple parts or feature points of an object.

[0065] For example, when the measured object is a chair, its three-dimensional information may include coordinate data of different parts of the chair, such as the four legs, the seat, and the backrest. The control module 11 needs to perform a comprehensive analysis on these data to ensure that the measurement results of the laser radar 1 can fully and accurately reflect the actual situation of the measured object.

[0066] Furthermore, the control module 11 performs Euclidean distance calculation on the difference between the three-dimensional information of the object under test output by the processing unit 13 and the three-dimensional information of the object under test transmitted by the control terminal 3 to obtain an error parameter. The Euclidean distance is a method for measuring the distance between two points in a multidimensional space. By calculation, a specific value can be obtained, which reflects the degree of difference between the two three-dimensional information. This error parameter is an important basis for the neural network unit 14 to calculate the adjustment amount.

[0067] Furthermore, the adjustment information transmitted by the control terminal 3 includes at least one set of error information and an adjustment amount that matches the error information. These adjustment information are preset and used to guide the neural network unit 14 to output corresponding adjustment amounts under specific error conditions. In this way, the neural network unit 14 can learn the best adjustment strategy under different error conditions, thereby improving the accuracy and efficiency of debugging.

[0068] In summary, the technical solution of this embodiment realizes the intelligence and automation of the laser radar 1 assembly and adjustment equipment by introducing the neural network unit 14. The neural network unit 14 can continuously optimize its output to adapt to different debugging environments and conditions by learning the relationship between the error parameters and the adjustment amount. This adaptive capability enables the laser radar 1 to adjust its posture more flexibly and efficiently in practical applications to meet the needs of various complex scenarios.

[0069] In addition, the control module 11 is connected to the display 15, so that the data and results of the debugging process can be displayed in real time, which is convenient for the operator to monitor and analyze the debugging status. Through the display 15, the operator can intuitively see the posture adjustment of the laser radar 1 and the change of the error parameters, so as to make corresponding adjustment decisions.

[0070] Example 2

[0071] like Figure 1-5As shown, the difference from Example 1 is: in order to further improve the recognition ability of the neural network unit 14 of the present application for the error parameters in the laser radar 1 test through the adjustment information transmitted by the control terminal 3. Furthermore, the neural network unit 14 of the present application includes a training set 141 and an additional set 142. The training set 141 is used to store the adjustment information transmitted by the control terminal 3; the additional set 142 is also used to store the adjustment amount information and error information output by the neural network unit 14. The training set 141 is the basis for the learning of the neural network unit 14. It contains a large amount of known error information and corresponding adjustment amounts. If the error information transmitted to the neural network unit 14 by the control module 11 matches the error information in the training set 141, the neural network unit 14 directly outputs the corresponding adjustment amount. The additional set 142 is used to store the error information encountered by the neural network unit 14 in actual operation.

[0072] Further, the error information in the training set 141 is represented by different categories by the designated first feature vector, and the error information in the additional set 142 is divided into different categories by the neural network unit 14 according to the similarity between the designated second feature vector and the first feature vector of the error information in the training set 141. In this way, the neural network unit 14 can match the new error information with the existing knowledge, so as to quickly find a suitable adjustment strategy. In the specific implementation process, the first feature vector and the second feature vector are mathematical models based on the error parameters, for example, they can be statistical characteristics of the error parameters, frequency distribution characteristics or other mathematical descriptions that can reflect the error characteristics. Through these feature vectors, the neural network unit 14 can classify and identify the error information, so as to improve the accuracy of the adjustment. Further, the neural network unit 14 confirms the similarity between the second vector of the error information in at least one additional set 142 and the first vector of the error information in any training set 141, and confirms the error information pointed to by the second vector with a similarity greater than the first threshold as the category represented by the first vector of the error information in the training set 141;

[0073] Further, the neural network unit 14 uses the adjustment amount associated with the error information of the same type as the error information pointed to by the second vector in the additional set 142 in the training set 141 as the output of the neural network unit 14. At the same time, the neural network unit 14 can store this new error information in the training set 141, further improving the neural network unit 14's ability to recognize and output the adjustment amount matching the error information.

[0074] In the specific implementation process, the working principle of the training set 141 and the additional set 142 in this embodiment is as follows: the neural network unit 14 first learns and establishes the mapping relationship between the error and the adjustment amount by analyzing the error information in the training set 141. When new error information appears, the neural network unit 14 will use the data in the additional set 142 to determine the category of the new error information by calculating the similarity between the second vector of the error information and the first vector of the error information in the training set 141. If the similarity exceeds the preset first threshold, the new error information is classified into the corresponding category, and the neural network unit 14 will output the adjustment amount associated with the error information of this category.

[0075] Furthermore, the above mechanism allows the neural network unit 14 to continuously learn and adapt to new error situations in actual operation, thereby improving the adaptability and accuracy of the debugging process. By continuously adding new error information and adjustment amounts to the additional set 142, the neural network unit 14 can continuously optimize its performance, making the posture adjustment of the laser radar 1 more accurate and efficient. In addition, through the connection between the control module 11 and the display 15, the operator can monitor the debugging process in real time, adjust the strategy in time, and ensure the debugging quality of the laser radar 1.

[0076] In summary, the technical solution of this embodiment realizes the intelligence and automation of the laser radar 1 assembly and adjustment equipment by introducing the neural network unit 14. The neural network unit 14 can continuously optimize its output by learning the relationship between the error parameters and the adjustment amount to adapt to different debugging environments and conditions. This adaptive ability enables the laser radar 1 to adjust its posture more flexibly and efficiently in practical applications to meet the needs of various complex scenes. In addition, the control module 11 is connected to the display 15, so that the data and results of the debugging process can be displayed in real time, which is convenient for the operator to monitor and analyze the debugging status. Through the display 15, the operator can intuitively see the posture adjustment of the laser radar 1 and the changes in the error parameters, so as to make corresponding adjustment decisions.

[0077] The other details that are the same as those in Example 1 are not described in detail in this example.

[0078] Example 3

[0079] like Figure 1-5 As shown, the difference from Example 1 is that: in order to further improve the accuracy and flexibility of laser radar 1 adjustment. This application designs a laser radar 1 adjustment method, including any of the above laser radar 1 adjustment equipment, the laser radar 1 adjustment method steps are:

[0080] S101, start the laser radar 1 installation and adjustment equipment to put the laser radar 1 in a test state; input the position information of the object to be measured through the control terminal 3, start the laser radar 1 to emit laser and receive the reflected signal;

[0081] S201, the processing unit 13 receives the signal of the laser receiver 122, and outputs the position information of the measured object to the control module 11 after processing; the control module 11 compares the position information output by the processing unit 13 with the position information input by the control terminal 3, and calculates the error parameter;

[0082] S301, the control module 11 transmits the error parameter to the neural network unit 14, and the neural network unit 14 outputs the adjustment amount to the debugging frame 2 according to the error parameter; the debugging frame 2 adjusts the posture of the laser radar 1 according to the adjustment amount transmitted by the control module 11;

[0083] S401, the neural network unit 14 records and analyzes the error parameters and adjustment amounts after each adjustment, which are used to optimize the neural network unit 14; after completing the installation and adjustment of the laser radar 1, the control module 11 synchronously displays the final adjustment results and error parameters through the display 15.

[0084] Specifically, through the above technical solution, the laser radar 1 installation and adjustment method can achieve fast and accurate posture adjustment.

[0085] Other details that are the same as those in Example 1 are not described in detail in this application.

[0086] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A laser radar assembly and adjustment device, characterized in that: It comprises a laser radar (1), a debugging frame (2) and a control terminal (3) which is communicatively connected to the laser radar (1); The laser radar (1) comprises a control module (11) and a measuring module (12); the measuring module (12) comprises a laser transmitter (121) and a laser receiver (122) and a processing unit (13) for processing information from the laser transmitter (121) and the laser receiver (122); the laser transmitter (121) is used to transmit laser light to an object to be measured; the laser receiver (122) is used to receive laser light transmitted back by the object to be measured; and the processing unit (13) is used to output position information of the object to be measured to the control module (11); The control module (11) compares the position information of the measured object transmitted by the control terminal (3) with the position information transmitted by the processing unit (13) to obtain an error parameter; the control module (11) also includes a neural network unit (14), and the neural network unit (14) outputs the adjustment amount of the laser radar (1) to the debugging frame (2) according to the error parameter; The control terminal (3) transmits adjustment information to the neural network unit (14), wherein the adjustment information is used to train the neural network unit (14) to output the corresponding adjustment amount; The debugging frame (2) is used to assemble the laser radar (1) and is electrically connected to the control module (11), and the debugging frame (2) adjusts the posture of the laser radar according to the adjustment amount transmitted by the neural network unit (14) in the control module (11).

2. The laser radar assembly and adjustment device according to claim 1, characterized in that: The position information of the measured object output by the processing unit (13) is three-dimensional information of at least one of the measured objects; and the position information of the measured object transmitted by the control terminal (3) is also three-dimensional information of at least one of the measured objects.

3. The laser radar assembly and adjustment device according to claim 2, characterized in that: The control module (11) is used to compare the three-dimensional information of the object to be measured output by the processing unit (13) with the three-dimensional information of the object to be measured transmitted by the control terminal (3) to output at least one error parameter.

4. The laser radar assembly and adjustment device according to claim 3, characterized in that: The control module (11) performs Euclidean distance calculation on the difference between the three-dimensional information of the object to be measured output by the processing unit (13) and the three-dimensional information of the object to be measured transmitted by the control terminal (3) to obtain an error parameter.

5. The laser radar assembly and adjustment equipment according to claim 1, characterized in that: The adjustment information transmitted by the control terminal (3) includes at least one set of the error information and an adjustment amount matching the error information.

6. The laser radar assembly and adjustment device according to claim 5, characterized in that: The neural network unit (14) includes a training set (141) and an additional set (142), wherein the training set (141) is used to store the adjustment information transmitted by the control terminal (3); and the additional set (142) is used to store the adjustment amount information and the error information output by the neural network unit (14).

7. The laser radar assembly and adjustment equipment according to claim 6, characterized in that: The error information in the training set (141) is divided into different categories represented by a designated first feature vector, and the error information in the additional set (142) is divided into different categories by the neural network unit (14) according to the similarity between the designated second feature vector and the first feature vector of the error information in the training set (141).

8. The laser radar assembly and adjustment equipment according to claim 7, characterized in that: The neural network unit (14) confirms the similarity between the second vector of the error information in at least one of the additional sets (142) and the first vector of the error information in any of the training sets (141), and confirms the error information pointed to by the second vector whose similarity is greater than a first threshold as the category represented by the first vector of the error information in the training set (141); The neural network unit (14) uses the adjustment amount associated with the error information in the training set (141) that is of the same type as the error information pointed to by the second vector in the additional set (142) as the output of the neural network unit (14).

9. The laser radar assembly and adjustment equipment according to claim 1, characterized in that: The control module (11) is electrically connected to the display (15).

10. A laser radar installation method, characterized in that: The laser radar adjustment device comprises the laser radar adjustment device according to any one of claims 1 to 9, wherein the steps of the laser radar adjustment method are as follows: S101, start the laser radar assembly and adjustment equipment to put the laser radar (1) in a test state; input the position information of the object to be measured through the control terminal (3), start the laser radar (1) to emit laser and receive reflected signals; S201, the processing unit (13) processes and outputs the position information of the measured object to the control module (11); the control module (11) compares the position information output by the processing unit (13) with the position information input by the control terminal (3), and calculates an error parameter; S301, the control module (11) transmits the error parameter to the neural network unit (14), and the neural network unit (14) outputs an adjustment amount to the debugging frame (2) according to the error parameter; the debugging frame (2) adjusts the posture of the laser radar (1) according to the adjustment amount transmitted by the control module (11); S401, the neural network unit (14) records and analyzes the error parameters and adjustment amounts after each adjustment, so as to optimize the neural network unit (14). After completing the installation and adjustment of the laser radar (1), the control module (11) synchronously displays the final adjustment result and the error parameters through the display (15).

Images