Method and system for removing raised dust by laser radar

Abstract

The invention discloses a method and system for removing raised dust by a laser radar, and belongs to the technical field of information processing, and the method comprises the steps: S1, carrying out the preprocessing of a point cloud obtained by the laser radar, and obtaining an obstacle set V; s2, performing penetrability analysis on each obstacle in the obstacle set V to obtain the penetration rate of the point cloud to each obstacle, and obtaining a first possibility P1 of dust raising according to the penetration rate; for each obstacle in the obstacle set V, calculating the variance of the reflection intensity, and obtaining a second possibility P2 of dust raising according to the variance; for each obstacle in the obstacle set V, solving the variance of each point normal vector included angle of the obstacle, and obtaining a third possibility P3 of dust raising according to the variance; and S3, for each obstacle in the obstacle set V, obtaining a final dust raising possibility P according to the P1, P2 and P3 of each obstacle, and when P is greater than a set threshold value, judging that dust raising exists.

Description

Technical Field

[0001] The present invention belongs to the field of information processing technology, and in particular relates to a method and system for removing dust from a laser radar. Background Art

[0002] Unmanned driving technology, as an innovative scientific and technological means, has been widely used in many mining areas, especially in the field of raw material transportation. The application of this technology not only significantly saves human resources, but also greatly improves the efficiency of transportation operations, while effectively reducing vehicle fuel consumption. Among the many components of unmanned driving technology, the perception module plays a vital role. It is located at the top level of the unmanned driving system and is mainly responsible for accurately modeling the environment around the vehicle using the vehicle's own sensors and information obtained from the outside. In this way, the perception module can provide detailed information about surrounding obstacles to the decision-making system, so that the unmanned vehicle can plan a safe driving path and ensure that the vehicle can move smoothly and safely along the established route. Therefore, the performance of the perception module largely determines the smoothness of the operation of the unmanned vehicle and its overall safety.

[0003] In the practice of unmanned driving technology, the perception sensors equipped on the vehicle itself mainly include cameras, laser radars and millimeter wave radars. In the unmanned driving application in the mining area, the camera is often unable to meet the requirements of 24-hour continuous operation due to the limitation of light conditions. Although the millimeter wave radar can detect dynamic obstacles, its recognition ability for static obstacles is limited. Therefore, in the unmanned driving of the mining area, laser radar has become the main perception sensor. It can not only meet the requirements of all-weather operation, but also accurately detect dynamic and static obstacles. However, due to the particularity of the mining environment, for example, during the operation of the vehicle, a large amount of dust often occurs on site. These clusters of dust are sometimes mistakenly identified as obstacles by traditional laser radar algorithms, resulting in unnecessary parking of unmanned mining trucks, thereby reducing transportation efficiency. When the vehicle is fully loaded with heavy objects, this misjudgment may even affect the safety of the unmanned vehicle. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for removing dust using a laser radar, thereby preventing dust from being identified as an obstacle.

[0005] To achieve the above-mentioned invention object, the first object of the present invention is to provide a method for removing dust by a laser radar, comprising the following steps:

[0006] S1. Preprocess the point cloud acquired by the laser radar, remove the point clouds with reflection intensity greater than the threshold in the ground point cloud and non-ground point cloud, and perform Euclidean conditional clustering based on reflection intensity on the remaining point clouds to obtain the obstacle set V;

[0007] S2. Possibility analysis, including:

[0008] Perform a penetration analysis on each obstacle in the obstacle set V to obtain the penetration rate of the point cloud to each obstacle, and obtain the first dust probability P1 of each obstacle according to the penetration rate;

[0009] For each obstacle in the obstacle set V, the variance of the reflection intensity is calculated, and the second probability P2 of dust emission of each obstacle is obtained according to the size of the variance;

[0010] For each obstacle in the obstacle set V, the variance of the normal vector angle of each point is calculated, and the third possibility P3 of dust emission of each obstacle is obtained according to the size of the variance;

[0011] S3. For each obstacle in the obstacle set V, the final dust raising possibility P is obtained according to P1, P2, and P3 of each obstacle itself. When P is greater than the set threshold, it is judged as dust raising, otherwise it is judged as an obstacle.

[0012] Preferably, S1 is specifically:

[0013] First, the point cloud acquired by the LiDAR is segmented to obtain ground point cloud and non-ground point cloud;

[0014] Then, extract the non-ground point cloud with reflection intensity i less than the threshold I min Points, get the candidate point cloud PC;

[0015] Finally, the candidate point cloud PC is subjected to conditional Euclidean clustering based on reflection intensity to obtain the obstacle set V.

[0016] Preferably, S2 comprises:

[0017] Analyze each point of an obstacle in the obstacle set V and find out which beam of light emitted by the laser radar each point belongs to; assume that the point cloud points of the Nth row of beams are taken as a set, and the number of points is recorded as M n , find the maximum angle of each line beam in the XOY plane in the laser radar coordinate system, recorded as A; according to the angular resolution R of the laser radar, the point cloud points that each line beam should have are:

[0018] N = A / R;

[0019] The penetration rate B of the set is calculated by the following formula:

[0020] B=1-(M1+M2+...+M n ) / (N1+N2+...+N n )

[0021] The first possibility of dust emission is P1=f1(B), where f1 is the conversion relationship between the two, M1 is the actual number of point clouds of the first line beam, M2 is the actual number of point clouds of the second line beam, and M n is the actual number of point clouds of the nth line, N1 is the number of point clouds that the first line should have, N2 is the number of point clouds that the second line should have, and N n is the number of point clouds that the nth line bundle should have, n is the number of line bundles, and the higher the penetration rate obtained, the higher the first possibility P1 of dust emission.

[0022] Preferably, S2 comprises:

[0023] Calculate the variance Z of the self-reflection intensity of each obstacle i :

[0024] Z i =1 / m((i1-i - )·(i1-i - )+...+(i m -i - )·(i m -i - ));

[0025] Where: m is the number of each obstacle point cloud, i1~i m is the reflection intensity of the obstacle, i - is the average value of the obstacle reflection intensity, calculated by the following formula:

[0026] i - =(i1+i2+...+i m ) / m;

[0027] The second possibility of dust emission is P2 = f2(Z i ), the conversion relationship between f2 and f3, the smaller the variance, the higher the second possibility P2 of dust.

[0028] Preferably, S2 comprises:

[0029] Define the obstacle as s, and the steps to obtain the normal vector are:

[0030] S401, search for the nearest neighboring point of the p-th point in s to find c nearest points;

[0031] S402, using the pth point in s and c nearest points to fit a plane using the least squares method;

[0032] S403, finding the value of the normal vector of the plane, denoted as Normalp;

[0033] Loop through S401 to S403 to sequentially find the values ​​of the normal vectors of all points of the obstacle s;

[0034] Find the angle between the normal vector of the zth point and the normal vectors of the surrounding points, and find the average value of the angle q p , calculate the average angle q1~q between the normal vector of each point and the normal vector of the surrounding points for each point of the obstacle s. p , o is the number of points in s, q - is the average value of the angles of all the obstacle points, for q1~q p Find the variance q z :

[0035] q z =1 / o((q1-q - )·(q1-q - )+...+(q p -q - )·(q p -q - ))

[0036] q - =1 / o(q1+q2+...+q p )

[0037] The third possibility of dust emission is P3 = f3 (q z ), f3 is the conversion relationship between the two, and the variance q z The larger it is, the higher the third possibility P3 of dust emission is.

[0038] Preferably, in S3:

[0039] P=u1·P1+u2·P2+u3·P3;

[0040] Among them, u1, u2, and u3 correspond to the coefficients of P1, P2, and P3 respectively, and u1>u2>u3.

[0041] A second object of the present invention is to provide a system for removing dust using a laser radar, comprising:

[0042] The preprocessing module preprocesses the point cloud acquired by the laser radar, removes the point clouds with reflection intensity greater than the threshold in the ground point cloud and non-ground point cloud, and performs Euclidean conditional clustering based on the reflection intensity on the remaining point clouds to obtain the obstacle set V;

[0043] The penetration analysis module performs penetration analysis on each obstacle in the obstacle set V, obtains the penetration rate of the point cloud to each obstacle, and obtains the first possibility P1 of dust emission for each obstacle according to the penetration rate;

[0044] The reflection intensity analysis module calculates the variance of the reflection intensity for each obstacle in the obstacle set V, and obtains the second possibility P2 of dust emission for each obstacle according to the size of the variance;

[0045] The point cloud direction analysis module calculates the variance of the normal vector angle of each point of each obstacle in the obstacle set V, and obtains the third possibility P3 of dust for each obstacle based on the size of the variance;

[0046] The dust judgment module obtains the final dust possibility P for each obstacle in the obstacle set V according to each obstacle's own P1, P2, and P3. When P is greater than the set threshold, it is judged as dust, otherwise it is judged as an obstacle.

[0047] The third object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned laser radar dust removal method.

[0048] The fourth object of the present invention is to provide a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned laser radar dust removal method.

[0049] The advantages and positive effects of this application are:

[0050] The present invention first divides the point cloud into ground point cloud and non-ground point cloud, and then analyzes the first possibility P1 of dust based on the penetration rate, the second possibility P2 of dust based on the reflection intensity, and the third possibility P3 of dust based on the direction of the point cloud; finally, the final possibility P of dust is obtained by fitting based on the first possibility P1 of dust, the second possibility P2 of dust, and the third possibility P3 of dust. After comparing the final possibility P of dust with the threshold, it can accurately determine whether it is dust. Obviously, the present invention has higher accuracy through multi-dimensional analysis and judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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.

[0052] Figure 1 A flow chart provided by a preferred embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram of dust penetration analysis of a special obstacle in a preferred embodiment of the present invention is shown;

[0054] Figure 3 A schematic diagram of the normal vector of the dust surface in a preferred embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 ;

[0057] In the first embodiment, a method for removing dust using a laser radar comprises the following steps:

[0058] The first step is to obtain the point cloud and preprocess the point cloud;

[0059] Use laser radar to obtain the point cloud of the surrounding environment of the unmanned mining vehicle, pre-process the obtained point cloud, and obtain the obstacle set V to be detected;

[0060] The preprocessing includes: firstly, performing ground segmentation on the point cloud to divide the point cloud into ground point cloud and non-ground point cloud; then extracting points in the non-ground point cloud whose reflection intensity i is less than a threshold, for example, the threshold can be selected as 30.0, to obtain a candidate point cloud pc; finally, performing conditional Euclidean clustering based on reflection intensity on the candidate point cloud pc to obtain an obstacle set V.

[0061] The second step is possibility analysis, which includes:

[0062] Perform a penetration analysis on each obstacle in the obstacle set V to obtain the first possibility P1 of dust emission.

[0063] In reality, some obstacles may be penetrated by the laser beam, resulting in a certain degree of penetration of the obstacles. Figure 2 , the laser beam emitted by the laser radar 1 passes through the obstacle 2 (the obstacle here is a ring structure), part of the beam is blocked, and part of the beam passes through the central through hole and irradiates the ground 3;

[0064] Perform penetration analysis on each obstacle in the obstacle set V to obtain the penetration rate of the laser radar on the obstacle. First, for each point of an obstacle in the obstacle set V, find out which beam of the laser radar it belongs to; assume that the point cloud points of the Nth beam are taken as a set, and the number of points is recorded as M n, find its maximum angle in the XOY plane in the laser radar coordinate system, recorded as A; according to the angular resolution R of the laser radar, the point cloud points that it should have in this line beam can be obtained as:

[0065] N = A / R;

[0066] The penetration rate B of the set is calculated by the following formula:

[0067] B=1-(M1+M2+...+M n ) / (N1+N2+...+N n )

[0068] The first possibility of dust emission is P1=f1(B), where f1 is the conversion relationship between the two, M1 is the actual number of point clouds of the first line beam, M2 is the actual number of point clouds of the second line beam, and M n is the actual number of point clouds of the nth line, N1 is the number of point clouds that the first line should have, N2 is the number of point clouds that the second line should have, and N n is the number of point clouds that the nth line bundle should have, n is the number of line bundles, and the higher the penetration rate obtained, the higher the first possibility P1 of dust emission.

[0069] The reflection intensity of each obstacle in the obstacle set V is analyzed to obtain the second possibility P2 of dust.

[0070] For each obstacle in the obstacle set V, the variance of its reflection intensity is calculated, and the second dust possibility P2 is obtained according to the size of the variance; since the reflection intensity of the laser radar depends on the incident angle of the laser and the material of the reflector, when the incident angle is not much different, since the material of the dust itself is the same, the reflection intensity value obtained should not be much different; calculate the variance value Z of the self-reflection intensity of each obstacle i :

[0071] Z i =1 / m((i1-i - )·(i1-i - )+...+(i m -i - )·(i m -i - ));

[0072] Where: m is the number of each obstacle point cloud, i1~i m is the reflection intensity of the obstacle, i - is the average value of the obstacle reflection intensity, calculated by the following formula:

[0073] i - =(i1+i2+...+i m ) / m;

[0074] The second possibility of dust emission is P2 = f2(Z i ), the conversion relationship between f2 and f3, the smaller the variance, the higher the second possibility P2 of dust.

[0075] Perform an inverse normal vector analysis on each obstacle in the obstacle set V to obtain the third possibility P3 of dust.

[0076] Figure 3 This is a typical dust map, which shows that the normal vector angle of the points on the dust surface is large.

[0077] For each obstacle in V, find its surface normal vector, and get the third possibility P3 of dust according to the consistency of the normal vector. Taking an obstacle s in V as an example, the steps to find the normal vector are as follows: first, search for the nearest neighboring point of the pth point in s and find the c nearest points. Then, use the pth point in s and the nearest c neighboring points to fit a plane using the least squares method. Finally, find the value of the normal vector of the plane, which is recorded as Normalp. According to the above method, the values ​​of the normal vectors of all points of obstacle s are found in turn.

[0078] Find the angle between the normal vector of the zth point and the normal vectors of the surrounding points, and find the average value of the angle q p , calculate the average angle q1~q between the normal vector of each point and the normal vector of the surrounding points for each point of the obstacle s. p , o is the number of points in s, q - is the average value of the angles of all the obstacle points, for q1~q p Find the variance q z :

[0079] q z =1 / o((q1-q - )·(q1-q - )+...+(q p -q - )·(q p -q - ))

[0080] q - =1 / o(q1+q2+...+q p )

[0081] The third possibility of dust emission is P3 = f3 (q z ), f3 is the conversion relationship between the two, and the variance q z The larger the value is, the higher the third possibility P3 of dust is. In the third step, the obstacles in the obstacle set V are finally judged as dust;

[0082] For each obstacle in the obstacle set V, the final dust possibility P is obtained based on its own P1, P2, and P3. When P is greater than 3.0, it can be judged as dust. From the field test, it can be seen that P1, P2, and P3 have different influences on the judgment of dust, so different coefficients need to be set to show this phenomenon. Different parameters can be set in the functions of f1, f2, and f3, but for the sake of clarity, they are specially set here for description. In the mining scenario, P1 has a better impact on the result than P2, and P 21 The effect on the result is better than P3. The coefficients u1, u2, and u3 are set to correspond to P1, P2, and P3. From the above, we can know that the size of u1, u2, and u3 is:

[0083] u1>u2>u3

[0084] Therefore, the final probability P of dust emission is:

[0085] P=u1·P1+u2·P2+u3·P3.

[0086] A second embodiment is a system for removing dust from a laser radar, which is used to implement the method of the first embodiment. The system includes:

[0087] The preprocessing module uses the laser radar to obtain the point cloud of the surrounding environment of the unmanned mining truck, preprocesses the point cloud obtained by the laser radar, removes the point cloud with reflection intensity greater than the threshold in the ground point cloud and non-ground point cloud, and performs Euclidean conditional clustering based on the reflection intensity on the remaining point cloud to obtain the obstacle set V; the preprocessing may include: ground segmentation, filtering and clustering processing.

[0088] The penetration analysis module performs penetration analysis on each obstacle in the obstacle set V, obtains the penetration rate of the point cloud to each obstacle, and obtains the first possibility P1 of dust according to the size of the penetration rate;

[0089] The reflection intensity analysis module solves the variance of the reflection intensity of all points of each obstacle in the obstacle set V one by one, and obtains the second possibility P2 of dust emission for each obstacle according to the size of the variance;

[0090] The point cloud direction analysis module solves the normal vectors of all points and the points around each obstacle in the obstacle set V one by one, and solves the variance of the angles of all normal vectors. According to the size of the variance, the third possibility P3 of dust emission for each obstacle is obtained;

[0091] The dust judgment module obtains the final dust possibility P for each obstacle in the obstacle set V according to each obstacle's own P1, P2, and P3. When P is greater than the set threshold, it is judged as dust, otherwise it is judged as non-dust.

[0092] A third embodiment is a computer-readable storage medium storing a computer program, which implements the above-mentioned laser radar dust removal method when executed by a processor.

[0093] A fourth embodiment is a computer program product, comprising a computer program, which implements the above-mentioned laser radar dust removal method when executed by a processor.

[0094] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for removing dust using a laser radar, characterized in that: include: S1. Preprocess the point cloud acquired by the laser radar, remove the point clouds with reflection intensity greater than the threshold in the ground point cloud and non-ground point cloud, and perform Euclidean conditional clustering based on reflection intensity on the remaining point clouds to obtain the obstacle set V; S2. Possibility analysis, including: Perform a penetration analysis on each obstacle in the obstacle set V to obtain the penetration rate of the point cloud to each obstacle, and obtain the first dust probability P1 of each obstacle according to the penetration rate; For each obstacle in the obstacle set V, the variance of the reflection intensity is calculated, and the second probability P2 of dust emission of each obstacle is obtained according to the size of the variance; For each obstacle in the obstacle set V, the variance of the normal vector angle of each point is calculated, and the third possibility P3 of dust emission of each obstacle is obtained according to the size of the variance; S3. For each obstacle in the obstacle set V, the final dust raising possibility P is obtained according to P1, P2, and P3 of each obstacle itself. When P is greater than the set threshold, it is judged as dust raising, otherwise it is judged as an obstacle.

2. The method for removing dust by laser radar according to claim 1, characterized in that: S1 is specifically: First, the point cloud acquired by the LiDAR is segmented to obtain ground point cloud and non-ground point cloud; Then, extract the non-ground point cloud with reflection intensity i less than the threshold I min Points, get the candidate point cloud PC; Finally, the candidate point cloud PC is subjected to conditional Euclidean clustering based on reflection intensity to obtain the obstacle set V.

3. The method for removing dust from a laser radar according to claim 1, characterized in that S2 include: Analyze each point of an obstacle in the obstacle set V and find out which beam of light emitted by the laser radar each point belongs to; assume that the point cloud points of the Nth row of beams are taken as a set, and the number of points is recorded as M n , find the maximum angle of each line beam in the XOY plane in the laser radar coordinate system, recorded as A; according to the angular resolution R of the laser radar, the point cloud points that each line beam should have are: N = A / R; The penetration rate B of the set is calculated by the following formula: B=1-(M1+M2+...+M n ) / (N1+N2+...+N n ); The first possibility of dust emission is P1=f1(B), where f1 is the conversion relationship between the two, M1 is the actual number of point clouds of the first line beam, M2 is the actual number of point clouds of the second line beam, and M n is the actual number of point clouds of the nth line, N1 is the number of point clouds that the first line should have, N2 is the number of point clouds that the second line should have, and N n is the number of point clouds that the nth line bundle should have, n is the number of line bundles, and the higher the penetration rate obtained, the higher the first possibility P1 of dust emission.

4. The method for removing dust from a laser radar according to claim 1, characterized in that S2 include: Calculate the variance Z of the self-reflection intensity of each obstacle i : Z i =1 / m((i1-i - )·(i1-i - )+...+(i m -i - )·(i m -i - )); Where: m is the number of each obstacle point cloud, i1~i m is the reflection intensity of the obstacle, i - is the average value of the obstacle reflection intensity, calculated by the following formula: i - =(i1+i2+...+i m ) / m; The second possibility of dust emission is P2 = f2(Z i ), the conversion relationship between f2 and f3, the smaller the variance, the higher the second possibility P2 of dust.

5. The method for removing dust from a laser radar according to claim 1, characterized in that S2 include: Define the obstacle as s, and the steps to obtain the normal vector are: S401, search for adjacent points of the p-th point in s to find c closest points; S402, using the pth point in s and c nearest points to fit a plane using the least squares method; S403, finding the value of the normal vector of the plane, denoted as Normalp; Loop through S401 to S403 to sequentially find the values ​​of the normal vectors of all points of the obstacle s; Find the angle between the normal vector of the zth point and the normal vectors of the surrounding points, and find the average value of the angle q p , calculate the average angle q1~q between the normal vector of each point and the normal vector of the surrounding points for each point of the obstacle s. p , o is the number of points in s, q - is the average value of the angles of all the obstacle points, for q1~q p Find the variance q z : q z =1 / o((q1-q - )·(q1-q - )+...+(q p -q - )·(q p -q - )) q - =1 / o(q1+q2+...+q p ) The third possibility of dust emission is P3 = f3 (q z ), f3 is the conversion relationship between the two, and the variance q z The larger it is, the higher the third possibility P3 of dust emission is.

6. The method for removing dust by laser radar according to claim 1, characterized in that: In S3: P=u1·P1+u2·P2+u3·P3; Among them, u1, u2, and u3 correspond to the coefficients of P1, P2, and P3 respectively, and u1>u2>u3.

7. A laser radar dust removal system, characterized in that: include: The preprocessing module preprocesses the point cloud acquired by the laser radar, removes the point clouds with reflection intensity greater than the threshold in the ground point cloud and non-ground point cloud, and performs Euclidean conditional clustering based on the reflection intensity on the remaining point clouds to obtain the obstacle set V; The penetration analysis module performs penetration analysis on each obstacle in the obstacle set V, obtains the penetration rate of the point cloud to each obstacle, and obtains the first possibility P1 of dust emission for each obstacle according to the penetration rate; The reflection intensity analysis module calculates the variance of the reflection intensity for each obstacle in the obstacle set V, and obtains the second possibility P2 of dust emission for each obstacle according to the size of the variance; The point cloud direction analysis module calculates the variance of the normal vector angle of each point of each obstacle in the obstacle set V, and obtains the third possibility P3 of dust for each obstacle based on the size of the variance; The dust judgment module obtains the final dust possibility P for each obstacle in the obstacle set V according to each obstacle's own P1, P2, and P3. When P is greater than the set threshold, it is judged as dust, otherwise it is judged as an obstacle.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for removing dust from a laser radar as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method for removing dust from a laser radar as described in any one of claims 1 to 6 is implemented.

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