Occlusion detection method, device, equipment and computer storage medium

Abstract

The present application proposes an occlusion detection method, an occlusion detection device, an occlusion detection device and a computer storage medium. The occlusion detection method includes: obtaining a laser point cloud and a laser origin in a world coordinate system; obtaining a target area of ​​a target to be identified in a world coordinate system; traversing the line segment between each data point and the laser origin in the laser point cloud, and obtaining the relative position relationship between the line segment and the target area; obtaining an observed quantity based on the relative position relationship of all line segments; and determining the occlusion state of the target area using the observed quantity and the observable quantity determined by the number of data points in the laser point cloud. The above-mentioned occlusion detection method does not rely on prior sensor calibration, flexibly combines the sensor with the target area to be identified, and dynamically generates an observation range. It can be applied to occlusion judgments of different distances, sizes and shapes. By integrating the observed target into the occlusion judgment logic, the judgment result is more accurate, reliable and more versatile.

Description

Technical Field

[0001] The present application relates to the field of positioning perception technology, and in particular to an occlusion detection method, an occlusion detection device, an occlusion detection equipment and a computer storage medium. Background Art

[0002] In the fields of logistics and industry, mobile robots generally refer to automated guided vehicles (AGVs), which are equipped with automatic guidance devices such as electromagnetic or optical devices, can travel along a specified guidance path, and have safety protection and transfer functions. They can replace manual labor to lift and carry containers such as shelves.

[0003] In related technologies, AGVs and robots are increasingly used in various flexible production links in industrial scenarios, which relies on the stable and accurate recognition and perception capabilities of various sensors of AGVs and robots for the surrounding environment. In the dynamic environment of industrial production where humans and machines coexist, there are many unexpected variables that need to be overcome, including problems such as sensors being blocked by non-standard transport objects and target storage locations being occupied by humans. Summary of the invention

[0004] In order to solve the above technical problems, the present application proposes an occlusion detection method, an occlusion detection device, an occlusion detection equipment and a computer storage medium.

[0005] In order to solve the above technical problems, the present application proposes an occlusion detection method, which includes:

[0006] Get the laser point cloud and laser origin in the world coordinate system;

[0007] Obtain a target area of ​​the target to be identified in the world coordinate system;

[0008] Traversing the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area;

[0009] Based on the relative position relationship of all line segments, the observed values ​​are obtained;

[0010] The occlusion state of the target area is determined by using the observed quantity and the observable quantity determined by the number of data points of the laser point cloud.

[0011] Wherein, obtaining the observed value based on the relative position relationship of all line segments includes:

[0012] Initializing the count of the observed quantity;

[0013] Each line segment is traversed, and when any line segment intersects with the target area and / or is located in the target area, the count of the observed quantity is accumulated.

[0014] Wherein, the occlusion detection method further includes:

[0015] Constructing a plurality of region edges using adjacent vertices of the target region;

[0016] Determine whether there is at least one area edge among the plurality of area edges that intersects with the line segment;

[0017] If so, it is determined that the line segment intersects the target area.

[0018] Wherein, the occlusion detection method further includes:

[0019] Get the first endpoint and the second endpoint of the line segment;

[0020] Determine whether the first endpoint and the second endpoint are both within the target area;

[0021] If so, it is confirmed that the line segment is located within the target area.

[0022] The traversing of the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area includes:

[0023] Traversing the line segment between each data point in the laser point cloud and the laser origin to obtain the observation angle between the line segment and the observation boundary;

[0024] In response to the observation angle being smaller than a maximum observation angle, a relative position relationship between the line segment and the target area is acquired.

[0025] Wherein, the occlusion detection method further includes:

[0026] Obtaining the connecting lines between the laser origin and each vertex of the target area;

[0027] Obtaining the maximum angle between any two connecting lines as the maximum observation angle;

[0028] The two connecting lines forming the maximum observation angle are used as the observation boundaries.

[0029] Wherein, the determining the occlusion state of the target area by using the observed amount and the observable amount determined by the number of data points of the laser point cloud includes:

[0030] Based on the ratio of the observed quantity to the observable quantity, obtaining the observation confidence of the target area;

[0031] In response to the observation confidence being less than a preset threshold, determining that the occlusion state of the target area is occlusion exists.

[0032] In order to solve the above technical problems, the present application also proposes an occlusion detection device, which includes: an acquisition module, a calculation module, a counting module, and a detection module; wherein:

[0033] The acquisition module is used to acquire the laser point cloud and the laser origin in the world coordinate system;

[0034] The acquisition module is used to acquire a target area of ​​the target to be identified in the world coordinate system;

[0035] The calculation module is used to traverse the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area;

[0036] The counting module is used to obtain the observed value based on the relative position relationship of all line segments;

[0037] The detection module is used to determine the occlusion state of the target area by using the observed quantity and the observable quantity determined by the number of data points of the laser point cloud.

[0038] In order to solve the above technical problems, the present application also proposes an occlusion detection device, which includes a memory and a processor coupled to the memory; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the occlusion detection method as described above.

[0039] In order to solve the above technical problem, the present application also proposes a computer storage medium, which is used to store program data. When the program data is executed by a computer, it is used to implement the above occlusion detection method.

[0040] Compared with the prior art, the beneficial effects of the present application are: the occlusion detection device obtains the laser point cloud and the laser origin in the world coordinate system; obtains the target area of ​​the target to be identified in the world coordinate system; traverses the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area; obtains the observed quantity based on the relative position relationship of all line segments; and determines the occlusion state of the target area using the observed quantity and the observable quantity determined by the number of data points in the laser point cloud. The above-mentioned occlusion detection method does not rely on prior sensor calibration, flexibly combines the sensor with the target area to be identified, and dynamically generates the observation range. It can be applied to occlusion judgments of different distances, sizes, and shapes. By integrating the observed target into the occlusion judgment logic, the judgment result is more accurate, reliable, and more versatile. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] in:

[0043] Figure 1 It is a flowchart of an embodiment of an occlusion detection method provided by the present application;

[0044] Figure 2 It is a schematic diagram of the overall process of the occlusion detection method provided by this application;

[0045] Figure 3 is a structural schematic diagram of an embodiment of a mobile robot provided by the present application;

[0046] Figure 4 It is a schematic diagram of an embodiment of an observable area of ​​a target to be identified provided by the present application;

[0047] Figure 5 is a schematic diagram of an embodiment of a connection edge set provided by the present application;

[0048] Figure 6 is a schematic diagram of an embodiment of an observable range provided by the present application;

[0049] Figure 7 yes Figure 1 The flowchart of step S14 of the occlusion detection method is shown;

[0050] Figure 8 is a structural schematic diagram of an embodiment of an occlusion detection device provided by the present application;

[0051] Fig. 9 is a structural schematic diagram of an embodiment of an occlusion detection device provided by the present application;

[0052] Fig.10 It is a structural diagram of an embodiment of a computer storage medium provided by the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] To solve the problems of the prior art, this application proposes a laser radar occlusion detection method that does not rely on prior sensor calibration. This application flexibly combines the sensor with the target area to be identified, dynamically generates the observation range, and can be applied to occlusion judgments of different distances, sizes, and shapes. This application integrates the observed target into the occlusion judgment logic, making the judgment result more accurate, reliable, and more versatile, and can well distinguish between obstacle occlusion and target objects.

[0056] Please refer to Figure 1 and Figure 2 , Figure 1 is a flow chart of an embodiment of an occlusion detection method provided by the present application. Figure 2 It is a schematic diagram of the overall process of the occlusion detection method provided by this application.

[0057] The occlusion detection method of the present application is applied to an occlusion detection device, wherein the occlusion detection device of the present application can be a server, a terminal device, or a system in which a server and a terminal device cooperate with each other. Accordingly, the various parts included in the occlusion detection device, such as various units, subunits, modules, and submodules, can all be set in the server, can all be set in the terminal device, or can be set in the server and the terminal device respectively.

[0058] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here.

[0059] Specifically, the occlusion detection device of the present application may be a mobile robot based on an odometer and a lidar, or a processing unit on a mobile robot, etc.

[0060] like Figure 3 As shown, Figure 3 Schematic diagram of the structure of a mobile robot according to an embodiment of the present application. Figure 3 As shown, the mobile robot used in this application includes a mobile chassis (including but not limited to a motion controller, a motor, a battery, an embedded computer, an odometer, etc.), a lidar sensor, etc.

[0061] Basic function introduction: The robot performs high-precision positioning in a priori maps generated based on the warehouse environment. The map includes components such as shelves, workbenches, house support frames, and goods to be moved. The mobile robot described in this application uses an odometer to estimate the change in the robot's movement and uses a laser radar to scan the environment contour. When the robot enters the designated target recognition area, it will perform target detection and recognition.

[0062] like Figure 1 As shown, the specific steps are as follows:

[0063] Step S11: Obtain the laser point cloud and laser origin in the world coordinate system.

[0064] In the embodiments of the present application, Figure 2 As described above, the occlusion detection device navigates the robot to a specified position, and the position of the robot is obtained by calculating the odometer.

[0065] Then, before the occlusion detection device performs occlusion detection, in order to improve the accuracy of the data points, the laser point cloud data can be preprocessed. The specific process is as follows:

[0066] The occlusion detection device receives the odometer data and a frame of laser point cloud data, and stores the odometer data in a queue. t The barcode detection device calculates the laser point cloud data S according to the linear velocity v and angular velocity w of the odometers O1 and O2. t Remove motion distortion.

[0067] Furthermore, the occlusion detection device can also remove the trailing point cloud in the laser point cloud, and cluster the laser point cloud to filter out clusters with too few point clouds.

[0068] After preprocessing the laser point cloud data, the occlusion detection device converts the laser point cloud from the camera coordinate system to the world coordinate system to facilitate comparison with the area where the target to be identified is located.

[0069] Specifically, the occlusion detection device obtains the current position and posture of the vehicle body in the world coordinate system Obstruction detection device obtains laser extrinsic parameters And use posture and laser external reference Convert the laser point cloud to the world coordinate system

[0070] Step S12: Obtain the target area of ​​the target to be identified in the world coordinate system.

[0071] In an embodiment of the present application, the occlusion detection device constructs an observable area based on the posture and model size of the target object to be identified and the laser origin.

[0072] Specifically, the occlusion detection device obtains the position and model size of the current target object to be identified, and converts the target area into a polygonal area V in the world coordinate system. poly ={p1,p2,...p n},like Figure 4 As shown in the dotted rectangular box. Figure 4 It is a schematic diagram of an embodiment of the observable area of ​​the target to be identified provided by the present application.

[0073] Then, the occlusion detection device uses the posture and laser external reference The laser origin p origin Convert to the world coordinate system as Figure 4 As shown in the middle triangle pattern.

[0074] Step S13: traverse the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area.

[0075] In an embodiment of the present application, the occlusion detection device traverses the line segment between each data point and the laser origin in the laser point cloud to obtain the relative position relationship between the line segment and the target area. Among them, the principle of detecting occlusion in the occlusion detection method of the present application is that if there is no occlusion, the observation point should fall within the target area, or the line connecting the observation point and the laser origin passes through the target area. Based on the above principle and the relative position relationship between each data point of the laser point cloud and the target area, the occlusion detection device can obtain the occlusion information displayed by each data point.

[0076] Specifically, the occlusion detection device sets the laser origin p start With polygonal area V poly ={p1,p2,...p n} are connected to form a set of connected edges E. For details, please refer to Figure 5 , Figure 5It is a schematic diagram of an embodiment of a connection edge set provided by the present application.

[0077] like Figure 5 As shown, the occlusion detection device traverses the set E, calculates the angle orientation of each edge, and obtains the maximum angle range θ formed by these edges. This angle range is the observable range generated by the current laser combined with the target area. For details, please refer to Figure 6 , Figure 6 It is a schematic diagram of an embodiment of the observable range provided by the present application.

[0078] Furthermore, the occlusion detection device needs to determine the validity of observations of all point clouds within the observable range. First, the occlusion detection device needs to determine whether the data points of the laser point cloud are within the following range: Figure 6 within the observable range shown.

[0079] Specifically, the occlusion detection device traverses the laser point cloud P W , for each point cloud p i Create a laser origin p start To the current observation point cloud p i The line segment l i The occlusion detection device calculates the line segment l i and Figure 6 The maximum angle between the observation boundaries shown judge Is it within the observable range θ? If not, skip step S14. If it is within the observable range θ, proceed to step S14.

[0080] Step S14: Based on the relative position relationship of all line segments, the observed values ​​are obtained.

[0081] In the embodiment of the present application, the occlusion detection device traverses all line segments, calculates the number of line segments whose relative position relationships satisfy the conditions established according to the above-mentioned occlusion detection principle, and obtains the observed amount.

[0082] Please refer to Figure 7 , Figure 7 yes Figure 1 The flowchart of step S14 of the occlusion detection method is shown.

[0083] like Figure 7 As shown, the specific steps are as follows:

[0084] Step S141: Initialize the count of observed quantities.

[0085] In the embodiment of the present application, the occlusion detection device initializes the observed amount n and the observable amount m.

[0086] Step S142: traverse each line segment, and when any line segment intersects with the target area and / or is located in the target area, accumulate the count of the observed amount.

[0087] In the embodiment of the present application, the occlusion detection device determines that the line segment l i Is it consistent with polygon V poly ={p1,p2,...p n} intersect, the specific process is as follows:

[0088] The occlusion detection device detects the polygon V poly ={p1,p2,...p n}Build edges between all adjacent vertices to obtain a set of edges E poly Then, the occlusion detection device traverses the set E poly , for each edge e j Determine whether it is consistent with line segment l i If e j With l i If they intersect, then it means that l i With polygon V poly intersect.

[0089] Finally, if we confirm that line segment l i With polygon V poly intersect, then the observed quantity n = n+1.

[0090] Occlusion detection device line segment l i Is it in polygon V? poly ={p1,p2,...p n}, the specific process is as follows:

[0091] The occlusion detection device takes line segment l i Endpoint A and endpoint B, determine whether endpoint A and endpoint B are both in polygon V poly ={p1,p2,...p n}. If both endpoints A and B are in polygon V poly ={p1,p2,...p n}, then it means that line segment l i In polygon V poly ={p1,p2,...p n}Inside.

[0092] Finally, if we confirm that line segment l i Polygon V poly ={p1,p2,...p n}, then the observed quantity n = n + 1.

[0093] It should be noted that, for each line segment, as long as it meets any one of the above two conditions, the observed value will be accumulated by 1.

[0094] Furthermore, each time the occlusion detection device traverses a line segment, it executes: updating the observable quantity m=m+1. Therefore, the final observable quantity is the number of data points in the laser point cloud that are within the observable range.

[0095] Step S15: Determine the occlusion state of the target area using the observed quantity and the observable quantity determined by the number of data points of the laser point cloud.

[0096] In the embodiment of the present application, the occlusion detection device calculates the observation confidence of the target area When p is less than a certain threshold (such as 0.5), it is determined that the observation of the target area by the frame point cloud is not complete enough, indicating that there is an occlusion in the field of view between the current laser and the target area.

[0097] Furthermore, if the occlusion detection device determines that there is occlusion, it observes and determines repeatedly for multiple times; if occlusion still exists after repeated multiple times, the occlusion state is reported.

[0098] In the present application, the occlusion detection device obtains the laser point cloud and the laser origin in the world coordinate system; obtains the target area of ​​the target to be identified in the world coordinate system; traverses the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area; obtains the observed quantity based on the relative position relationship of all the line segments; and determines the occlusion state of the target area using the observed quantity and the observable quantity determined by the number of data points in the laser point cloud. The above-mentioned occlusion detection method does not rely on prior sensor calibration, flexibly combines the sensor with the target area to be identified, and dynamically generates the observation range. It can be applied to occlusion judgments of different distances, sizes, and shapes. By integrating the observed target into the occlusion judgment logic, the judgment result is more accurate, reliable, and more versatile.

[0099] The occlusion detection method of the present application does not rely on prior sensor calibration (no need to set a distance threshold), and does not require additional calibration costs and implementation costs.

[0100] The occlusion detection method of the present application is applicable to dynamic environment scenarios of general business. It flexibly combines the sensor with the target area to be identified, dynamically generates the observation range, and can be applicable to occlusion judgments of different distances, sizes, and shapes; the observed target is integrated into the occlusion judgment logic to make the judgment result more accurate, reliable and more versatile; and can well distinguish between obstacle occlusion and target objects.

[0101] The occlusion detection method of the present application can be applied to occlusion detection of all ranging sensors; the above embodiment is based on 2D laser and can be easily expanded to 3D laser.

[0102] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0103] In order to implement the above-mentioned occlusion detection method, the present application also proposes an occlusion detection device, for details, please refer to Figure 8 , Figure 8 It is a structural schematic diagram of an embodiment of an occlusion detection device provided in the present application.

[0104] The occlusion detection device 500 of this embodiment includes: an acquisition module 51 , a calculation module 52 , a counting module 53 , and a detection module 54 .

[0105] The acquisition module 51 is used to acquire the laser point cloud and the laser origin in the world coordinate system.

[0106] The acquisition module 51 is used to acquire the target area of ​​the target to be identified in the world coordinate system.

[0107] The calculation module 52 is used to traverse the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area.

[0108] The counting module 53 is used to obtain the observed value based on the relative position relationship of all line segments.

[0109] The detection module 54 is used to determine the occlusion state of the target area by using the observed amount and the observable amount determined by the number of data points of the laser point cloud.

[0110] In order to implement the above-mentioned occlusion detection method, this application also proposes an occlusion detection device, for details, please refer to Fig. 9 , Fig. 9 It is a structural schematic diagram of an embodiment of an occlusion detection device provided in the present application.

[0111] The occlusion detection device 400 of this embodiment includes a processor 41 , a memory 42 , an input / output device 43 , and a bus 44 .

[0112] The processor 41 , the memory 42 , and the input / output device 43 are respectively connected to the bus 44 . The memory 42 stores program data, and the processor 41 is used to execute the program data to implement the occlusion detection method described in the above embodiment.

[0113] In the embodiment of the present application, the processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having the ability to process signals. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or the processor 41 may also be any conventional processor, etc.

[0114] This application also provides a computer storage medium, please continue to refer to Fig.10 , Fig.10 6 is a schematic diagram of the structure of an embodiment of a computer storage medium provided in the present application. The computer storage medium 600 stores a computer program 61. When the computer program 61 is executed by a processor, it is used to implement the occlusion detection method of the above embodiment.

[0115] When the embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0116] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for detecting occlusion, characterized in that: The occlusion detection method comprises: Get the laser point cloud and laser origin in the world coordinate system; Obtain a target area of ​​the target to be identified in the world coordinate system; Traversing the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area; Based on the relative position relationship of all line segments, the observed values ​​are obtained; The occlusion state of the target area is determined by using the observed quantity and the observable quantity determined by the number of data points of the laser point cloud.

2. The occlusion detection method according to claim 1, characterized in that: The obtaining of observed values ​​based on the relative position relationship of all line segments includes: Initializing the count of the observed quantity; Each line segment is traversed, and when any line segment intersects with the target area and / or is located in the target area, the count of the observed quantity is accumulated.

3. The occlusion detection method according to claim 2, characterized in that: The occlusion detection method further includes: Constructing a plurality of region edges using adjacent vertices of the target region; Determine whether there is at least one area edge among the plurality of area edges that intersects with the line segment; If so, it is determined that the line segment intersects the target area.

4. The occlusion detection method according to claim 2, characterized in that: The occlusion detection method further includes: Get the first endpoint and the second endpoint of the line segment; Determine whether the first endpoint and the second endpoint are both within the target area; If so, it is confirmed that the line segment is located within the target area.

5. The occlusion detection method according to claim 1, characterized in that: The traversing the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area includes: Traversing the line segment between each data point in the laser point cloud and the laser origin to obtain the observation angle between the line segment and the observation boundary; In response to the observation angle being smaller than a maximum observation angle, a relative position relationship between the line segment and the target area is acquired.

6. The occlusion detection method according to claim 5, characterized in that: The occlusion detection method further includes: Obtaining the connecting lines between the laser origin and each vertex of the target area; Obtaining the maximum angle between any two connecting lines as the maximum observation angle; The two connecting lines forming the maximum observation angle are used as the observation boundaries.

7. The occlusion detection method according to claim 1, characterized in that: The determining the occlusion state of the target area by using the observed amount and the observable amount determined by the number of data points of the laser point cloud includes: Based on the ratio of the observed quantity to the observable quantity, obtaining the observation confidence of the target area; In response to the observation confidence being less than a preset threshold, determining that the occlusion state of the target area is occlusion exists.

8. An occlusion detection device, characterized in that: The occlusion detection device comprises: an acquisition module, a calculation module, a counting module, and a detection module; wherein, The acquisition module is used to acquire the laser point cloud and the laser origin in the world coordinate system; The acquisition module is used to acquire the target area of ​​the target to be identified in the world coordinate system; The calculation module is used to traverse the line segment between each data point in the laser point cloud and the laser origin to obtain the relative position relationship between the line segment and the target area; The counting module is used to obtain the observed value based on the relative position relationship of all line segments; The detection module is used to determine the occlusion state of the target area by using the observed quantity and the observable quantity determined by the number of data points of the laser point cloud.

9. An occlusion detection device, characterized in that: The occlusion detection device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the occlusion detection method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the occlusion detection method according to any one of claims 1 to 7.

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