A laser odometry method, system, device, and medium based on reflectance signatures

Abstract

Embodiments of the present application provide a laser odometry method, system, device and medium based on reflectivity characteristics, the method comprising: obtaining an initial pose of a vehicle according to vehicle inertial information; registering a target laser point cloud with a geometric feature point map according to the initial pose; determining target geometric feature points in the target laser point cloud; constructing a geometric residual matrix and a first Jacobian matrix; registering the target laser point cloud with a photometric feature point map according to the initial pose; extracting target photometric feature points in the target laser point cloud; constructing a photometric residual matrix and a second Jacobian matrix; constructing a target residual matrix and a target Jacobian matrix; iteratively optimizing the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose; and updating the geometric feature point map and the photometric feature point map based on the target pose and the target laser point cloud. The positioning accuracy of the laser odometry in a tunnel environment is ensured.

Description

Technical Field

[0001] The present invention relates to the field of laser radar detection, and in particular to a laser odometer method, system, equipment and medium based on reflectivity characteristics. Background Art

[0002] With the rapid development of vehicle-mounted positioning technology, laser odometry, as a key sensor technology, has been widely used in autonomous driving and vehicle navigation systems. It quickly and accurately acquires three-dimensional point cloud data of the environment, providing real-time and accurate pose estimation for the vehicle. However, in some environments with degraded geometric features, such as tunnels, the point cloud data returned by laser scanning often exhibits a high degree of similarity and redundancy, lacking significant geometric features such as lines, planes, or edges. This makes it difficult for laser odometry to accurately extract and match feature points in tunnels, resulting in inaccurate pose estimation and affecting positioning accuracy. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a laser odometry method, system, device, and medium based on reflectivity characteristics, aiming to ensure the positioning accuracy of the laser odometry in a tunnel environment.

[0004] A first aspect of an embodiment of the present application provides a laser odometer method based on reflectivity characteristics, the method comprising:

[0005] According to the collected vehicle inertia information, the initial position of the vehicle is obtained through integration operation;

[0006] According to the initial pose, registering the collected target laser point cloud with the geometric feature point map to obtain a first registration result;

[0007] Determine, according to the first registration result, a target geometric feature point in the target laser point cloud, wherein the target geometric feature point is a laser point with a planar feature in the target laser point cloud;

[0008] Construct the geometric residual matrix and the first Jacobian matrix based on the target geometric feature points, geometric feature point map and initial pose;

[0009] According to the initial pose, registering the target laser point cloud with the photometric feature point map to obtain a second registration result;

[0010] Extracting target photometric feature points with the highest reflectivity in each area of ​​the target laser point cloud;

[0011] Constructing a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose;

[0012] Constructing a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and constructing a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix;

[0013] Iteratively optimizing the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose;

[0014] Based on the target pose and target laser point cloud, a geometric feature point map and a photometric feature point map are updated.

[0015] Optionally, determining target geometric feature points in the target laser point cloud according to the first registration result includes:

[0016] According to the first registration result, a matching point group corresponding to the laser point in the target laser point cloud is extracted from the geometric feature point map, wherein the matching point group is a plurality of points in the geometric feature point map that are closest to the laser point;

[0017] Fitting the points in the matching point group to obtain a corresponding matching plane, and calculating the distance between the points in the matching point group and the matching plane;

[0018] Determine a target matching point group among all matching point groups based on the distances between the points in each matching point group and their corresponding matching planes, and the matching plane corresponding to the target matching point group is the target matching plane;

[0019] The laser points in the target laser point cloud corresponding to the target matching point group are determined as target geometric feature points.

[0020] Optionally, a geometric residual matrix and a first Jacobian matrix are constructed based on the target geometric feature points, the geometric feature point map, and the initial pose, including:

[0021] Determine the distance function from the target geometric feature point to the corresponding target matching plane in the geometric feature point map as the geometric residual function;

[0022] Based on each geometric residual function, a geometric residual matrix is ​​constructed;

[0023] Construct the first Jacobian matrix based on each geometric residual function and the initial pose.

[0024] Optionally, registering the target laser point cloud with the photometric feature point map according to the initial pose to obtain a second registration result includes:

[0025] The target laser point cloud is converted into a two-dimensional laser point map through a coordinate system conversion function;

[0026] According to the initial posture, the two-dimensional laser point image is registered with the photometric feature point map to obtain a second registration result.

[0027] Optionally, extracting target photometric feature points with the highest reflectivity in each area of ​​the target laser point cloud includes:

[0028] Taking the reflectivity of each point in the two-dimensional laser point image corresponding to the target laser point cloud as a grayscale value, converting the two-dimensional laser point image into a two-dimensional reflectivity grayscale image;

[0029] According to the grayscale gradient of each point in the reflectivity grayscale two-dimensional map, the highest grayscale value point of each local area is extracted, and the point in the laser point two-dimensional map corresponding to the highest grayscale value point is used as the target photometric feature point.

[0030] Optionally, constructing a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map, and the initial pose includes:

[0031] With the target photometric feature point as the center, a target window of preset size is constructed in the two-dimensional image of the laser point;

[0032] According to the second registration result, a matching window corresponding to the target window is constructed in the photometric feature point map, wherein the matching window is a window in the photometric feature point map having the same size and position as the target window;

[0033] Determine a reflectivity difference function between a point in the target window and a point in the corresponding matching window as a photometric residual function;

[0034] According to each photometric residual function, a photometric residual matrix is ​​constructed;

[0035] The second Jacobian matrix is ​​constructed based on the photometric residual functions, coordinate system conversion functions and initial poses.

[0036] Optionally, updating a geometric feature point map and a photometric feature point map based on the target pose and the target laser point cloud includes:

[0037] Based on the target pose, the target laser point cloud is registered with the geometric feature point map, and new target geometric feature points in the target laser point cloud are determined according to the registration result;

[0038] Adding the new target geometric feature point to the geometric feature point map, and adjusting the position of each geometric feature point in the geometric feature point map according to the target posture;

[0039] Based on the target posture, the target laser point cloud is aligned with the photometric feature point map, the target photometric feature points are added to the photometric feature point map according to the alignment result, and the position of each photometric feature point in the photometric feature point map is adjusted according to the target posture.

[0040] A second aspect of an embodiment of the present application provides a laser odometer system based on reflectivity characteristics, the system comprising:

[0041] A first calculation module is used to obtain the initial position and posture of the vehicle through integration calculation based on the collected vehicle inertia information;

[0042] A first registration module is used to register the collected target laser point cloud with the geometric feature point map according to the initial posture to obtain a first registration result;

[0043] A first feature point determination module is configured to determine a target geometric feature point in the target laser point cloud according to the first registration result, wherein the target geometric feature point is a laser point with a planar feature in the target laser point cloud;

[0044] A first construction module is used to construct a geometric residual matrix and a first Jacobian matrix according to the target geometric feature points, the geometric feature point map and the initial pose;

[0045] A second registration module is used to register the target laser point cloud with the photometric feature point map according to the initial pose to obtain a second registration result;

[0046] A second feature point determination module is used to extract the target photometric feature point with the highest reflectivity in each area of ​​the target laser point cloud;

[0047] A second construction module is configured to construct a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose;

[0048] A third construction module is configured to construct a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and to construct a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix;

[0049] A first optimization module is used to iteratively optimize the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose;

[0050] The first updating module is used to update the geometric feature point map and the photometric feature point map based on the target posture and the target laser point cloud.

[0051] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, a laser odometer method based on reflectivity features as described in the first aspect of the present application is implemented.

[0052] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a laser odometer method based on reflectivity features as described in the first aspect of the present application is implemented.

[0053] Compared with the prior art, this application has the following advantages:

[0054] The embodiments of the present application provide a laser odometry method, system, device, and medium based on reflectivity features. The geometric constraints for initial pose optimization are established through geometric features, and the photometric constraints for initial pose optimization are established through photometric features based on reflectivity. The geometric constraints and photometric constraints are integrated and used together for initial pose optimization, thereby avoiding the problem of decreased pose estimation accuracy due to insufficient geometric features in tunnel environments and ensuring the positioning accuracy of the laser odometry.

[0055] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments of the present application. 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 any creative work.

[0057] Figure 1 This is a flow chart of a laser odometer method based on reflectivity characteristics, shown as an embodiment of the present application;

[0058] Figure 2 This is a structural block diagram of a laser odometer system based on reflectivity characteristics, shown as an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0060] refer to Figure 1 , Figure 1The following is a flow chart of a laser odometer method based on reflectivity characteristics according to an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a laser odometer method based on reflectivity characteristics, including steps S1 to S10:

[0061] Step S1: Based on the collected vehicle inertia information, the initial position of the vehicle is obtained through integration operation.

[0062] In this embodiment, the vehicle is equipped with an on-board inertial measurement unit (IMU), which includes a three-axis gyroscope and a three-axis accelerometer. The on-board inertial measurement unit collects the acceleration and angular velocity of the vehicle in three directions, obtaining a total of six component data, namely the vehicle inertial information. By integrating the three angular velocity components, the vehicle's posture change is estimated, and by integrating the three acceleration components, the vehicle's position change is estimated. Based on the changes in the vehicle's posture and position, the vehicle's initial posture is ultimately determined. Due to factors such as hardware errors, random errors, and integrated cumulative errors, there is a certain difference between the initial posture obtained by the on-board inertial measurement unit and the actual vehicle posture.

[0063] Step S2: According to the initial posture, the collected target laser point cloud is registered with the geometric feature point map to obtain a first registration result.

[0064] In this embodiment, the vehicle is equipped with an onboard laser radar (LiDAR). The onboard LiDAR emits a laser beam and receives the reflected signal, forming a laser point cloud. This laser point cloud is then used to obtain three-dimensional information and reflectivity information about the surrounding environment. Because the laser point cloud is acquired in the form of frames, the vehicle itself is in constant motion while the onboard LiDAR is operating, causing distortion in each frame of the collected laser point cloud, affecting data reliability. Therefore, distortion compensation is performed on each frame of the laser point cloud based on the initial pose to eliminate the laser point cloud distortion caused by vehicle motion. The laser point cloud after distortion compensation is the target laser point cloud.

[0065] The geometric feature point map contains geometric feature points (such as edge lines, plane points, etc.) that can reflect the structure of the vehicle's external environment. These geometric feature points are obtained through feature point extraction from each frame of laser point cloud collected by the on-board lidar, and are continuously updated to the geometric feature point map through an incremental geometric feature point update process.

[0066] Specifically, the three-dimensional coordinates of each point in the target laser point cloud are adjusted according to the initial posture, so that the position state of each point in the target laser point cloud returns to the position state at the moment reflected by the current geometric feature point map (that is, the moment when the geometric feature points in the previous frame of laser point cloud are updated to the geometric feature point map), and the target laser point cloud is spatially overlapped with the geometric feature point map to obtain the first alignment result.

[0067] Step S3: determining target geometric feature points in the target laser point cloud according to the first registration result, wherein the target geometric feature points are laser points with planar features in the target laser point cloud.

[0068] In this embodiment, the target laser point cloud and the geometric feature point map are spatially overlapped. After obtaining the first registration result, each geometric feature point in the geometric feature point map is matched with the laser point in the target laser point cloud that is closest to it. The matched laser points are the target geometric feature points. The target geometric feature points reflect the three-dimensional position of each geometric feature point in the geometric feature point map at the moment when the target laser point cloud is collected.

[0069] Step S4: Construct a geometric residual matrix and a first Jacobian matrix according to the target geometric feature points, the geometric feature point map and the initial pose.

[0070] In this embodiment, the target geometric feature points are obtained by aligning the target laser point cloud with the geometric feature point map according to the initial pose. Therefore, there is a distance difference between the target geometric feature points and the geometric feature points in the geometric feature point map caused by the initial pose error. The distance difference between each target geometric feature point and the geometric feature point in the corresponding geometric feature point map is used as the geometric residual of each target geometric feature point, and a geometric residual matrix containing all geometric residuals is constructed. According to the geometric residual calculation function of each target geometric feature point, the partial derivative of each geometric residual calculation function with respect to each component in the initial pose is calculated to construct a first Jacobian matrix. The first Jacobian matrix reflects how the geometric residual of each target geometric feature point changes when each component in the initial pose changes.

[0071] Step S5: According to the initial posture, the target laser point cloud is registered with the photometric feature point map to obtain a second registration result.

[0072] The photometric feature point map contains photometric feature points that can reflect the locations of objects with high reflectivity in the vehicle's external environment (such as road signs, reflectors, etc.). These photometric feature points are obtained through feature point extraction from each frame of laser point cloud collected by the on-board lidar, and are continuously updated to the photometric feature point map through an incremental photometric feature point update process.

[0073] Specifically, the three-dimensional coordinates of each point in the target laser point cloud are adjusted according to the initial posture, so that the position state of each point in the target laser point cloud returns to the position state reflected by the current photometric feature point map (that is, the moment when the photometric feature points in the previous frame of laser point cloud are updated to the photometric feature point map), and the target laser point cloud is spatially overlapped with the photometric feature point map to obtain the second registration result.

[0074] Step S6: extracting target photometric feature points with the highest reflectivity in each area of ​​the target laser point cloud.

[0075] In this embodiment, the target laser point cloud contains the reflectivity information of each laser point. By comparing the reflectivity of each laser point in a local area, the laser point with the highest reflectivity in the area is extracted as the target photometric feature point of the area. The target laser point cloud is divided into several local areas, and the laser point with the highest reflectivity in each local area is extracted as the target photometric feature point of the target laser point cloud.

[0076] Step S7: constructing a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose.

[0077] In this embodiment, based on the second registration result of the target laser point cloud and the photometric feature point map, the target photometric feature point is matched with the photometric feature point in the photometric feature point map closest to the target photometric feature point, and the reflectivity difference between each target photometric feature point and the photometric feature point in the matched photometric feature point map is used as the photometric residual of each target photometric feature point, and a photometric residual matrix containing all photometric residuals is constructed. According to the photometric residual calculation function of each target photometric feature point, the partial derivative of each photometric residual calculation function with respect to each component in the initial pose is calculated, and a second Jacobian matrix is ​​constructed. The second Jacobian matrix reflects how the photometric residual of each target photometric feature point changes when each component in the initial pose changes.

[0078] Step S8: constructing a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and constructing a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix.

[0079] In this embodiment, geometric constraints and photometric constraints are used together to determine the accurate posture of the vehicle. Therefore, the geometric residual matrix and the photometric residual matrix after normalization parameter constraints are vertically merged to obtain the merged target residual matrix, which is calculated specifically in the following way:

[0080]

[0081] Where, e is the target residual matrix, e geois the geometric residual matrix, e photo is the photometric residual matrix, and α is the normalization parameter.

[0082] The first Jacobian matrix and the second Jacobian matrix after normalization parameter constraints are vertically merged to obtain the merged target Jacobian matrix, which is calculated as follows:

[0083]

[0084] Where J is the target residual matrix, J geo is the geometric residual matrix, J photo is the photometric residual matrix.

[0085] Step S9: Based on the target residual matrix and the target Jacobian matrix, the initial pose is iteratively optimized to obtain the target pose.

[0086] In this embodiment, the target pose is obtained by iteratively optimizing the initial pose using an iterative error state Kalman filter. The iterative error state Kalman filter continuously adjusts the components of the initial pose using an iterative optimization technique and updates the estimated target residual matrix based on the target Jacobian matrix until the residual estimate is minimized. The pose obtained at this point is the optimized target pose.

[0087] Step S10: Based on the target pose and target laser point cloud, update the geometric feature point map and the photometric feature point map.

[0088] In this embodiment, the target posture is obtained after iteratively optimizing the initial posture through iterative error state Kalman filtering, which can accurately reflect the actual posture of the vehicle when the target laser point cloud is collected.

[0089] According to the target posture, the target laser point cloud is realigned with the geometric feature point map, and new target geometric feature points are re-extracted, the new target geometric feature points are added to the geometric feature point map, and the three-dimensional coordinates of the geometric feature point map are adjusted according to the target posture, and the position state of each geometric feature point in the geometric feature point map is updated to the position state corresponding to the moment when the target laser point cloud is collected.

[0090] According to the target posture, the target laser point cloud is realigned with the photometric feature point map, and new target photometric feature points are re-extracted, the new target photometric feature points are added to the photometric feature point map, and the three-dimensional coordinates of the photometric feature point map are adjusted according to the target posture, and the position state of each photometric feature point in the photometric feature point map is updated to the position state corresponding to the moment when the target laser point cloud is collected.

[0091] An embodiment of the present application provides a laser odometry method based on reflectivity features, which establishes geometric constraints for initial pose optimization through geometric features, and establishes photometric constraints for initial pose optimization through photometric features based on reflectivity. The geometric constraints and photometric constraints are integrated and used together for initial pose optimization, thereby ensuring the accuracy of pose estimation and the positioning accuracy of the laser odometry.

[0092] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometry method based on reflectivity features. In this laser odometry method based on reflectivity features, step S3 determines the target geometric feature points in the target laser point cloud based on the first registration result, including steps S31 to S34:

[0093] Step S31: extracting a matching point group corresponding to the laser point in the target laser point cloud from the geometric feature point map according to the first registration result, wherein the matching point group is a plurality of points in the geometric feature point map that are closest to the laser point.

[0094] In this embodiment, the target laser point cloud and the geometric feature point map are spatially overlapped. After obtaining the first registration result, for each laser point in the target laser point cloud, several (e.g., 10) geometric feature points in the geometric feature point map that are closest to the laser point are used as matching points corresponding to the laser point to form a matching point group corresponding to the laser point.

[0095] Step S32: fitting the points in the matching point group to obtain a corresponding matching plane, and calculating the distance between the points in the matching point group and the matching plane.

[0096] In this embodiment, after extracting the matching point group corresponding to the laser point from the geometric feature point map, a matching plane is fitted according to the coordinates of each point in the matching point group so that the sum of the squares of the distance differences between each point in the matching point group and the matching plane is minimized, and the sum of the squares of the distance differences is calculated. The method of fitting the matching plane can be used, for example, to construct a plane equation group according to the coordinates of each point and then use the least squares method to approximate the solution, which is not limited here.

[0097] Step S33: determining a target matching point group in all matching point groups based on the distances between points in each matching point group and their corresponding matching planes, and the matching plane corresponding to the target matching point group is the target matching plane.

[0098] In this embodiment, after calculating the sum of the squares of the distance differences between the points in each matching point group and the corresponding matching plane, the matching point group whose sum of the squares of the distance differences is less than a preset threshold is used as the target matching point group. At this time, all points in the target matching point group can be regarded as belonging to the same plane, and the matching plane corresponding to the target matching point group is used as the target matching plane containing all the points in the target matching point group.

[0099] Step S34: Determine the laser points in the target laser point cloud corresponding to the target matching point group as target geometric feature points.

[0100] In this embodiment, after the target matching point group is determined, the laser points in the target laser point cloud corresponding to the target matching point group are the target geometric feature points. The target geometric feature points reflect the three-dimensional positions of each geometric feature point in the geometric feature point map at the moment when the target laser point cloud is collected.

[0101] An embodiment of the present application provides a laser odometry method based on reflectivity features. The method uses several geometric feature points in a geometric feature point map that are closest to a laser point in a laser point cloud as matching points of the laser point. Based on the distance difference between the matching plane fitted by the several geometric feature points and the several geometric feature points, the method determines whether the several geometric feature points are located in the same plane, and further determines whether the laser points in the laser point cloud corresponding to the several geometric feature points are target geometric feature points, thereby improving the accuracy of extracting target geometric feature points from the target laser point cloud.

[0102] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometer method based on reflectivity characteristics. In the laser odometer method based on reflectivity characteristics, step S4 includes steps S41 to S43:

[0103] Step S41: determining a distance function from a target geometric feature point to a corresponding target matching plane in a geometric feature point map as a geometric residual function.

[0104] In this embodiment, the method for determining the target geometric feature point and the target matching plane is as described in steps S31 to S34. After determining the target geometric feature point and the corresponding target matching plane, the distance function from the target geometric feature point to the corresponding target matching plane is used as the geometric residual function of the target geometric feature point, which is specifically calculated in the following manner:

[0105] cost geo =n T *p+d

[0106] Where n T is the transposed matrix of the normal vector of the target matching plane, p is the world coordinate of the target geometric feature point, and d is the intercept of the target matching plane equation.

[0107] Step S42: constructing a geometric residual matrix based on each geometric residual function.

[0108] In this embodiment, after obtaining the geometric residual function of each target geometric feature point, the three-dimensional coordinates of the corresponding target geometric feature point are substituted into the function, the geometric residual of each target geometric feature point is calculated, and a multi-row and single-column geometric residual matrix is ​​constructed.

[0109] Step S43: Construct a first Jacobian matrix based on each geometric residual function and the initial pose.

[0110] In this embodiment, the chain rule is used to construct the first Jacobian matrix based on each geometric residual function and the initial pose, which is calculated specifically in the following way:

[0111]

[0112] Where, is the partial derivative of the geometric residual function with respect to the coordinates of the target geometric feature points, is the partial derivative of the target geometric feature point coordinates with respect to each component in the initial pose.

[0113] An embodiment of the present application provides a laser odometry method based on reflectivity features. By using the distance function from the target geometric feature point to the corresponding target matching plane as the geometric residual function of the target geometric feature point, a geometric residual matrix and a first Jacobian matrix are constructed according to the geometric residual function and the initial pose, thereby improving the accuracy of constructing the geometric constraints of the laser odometry.

[0114] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometer method based on reflectivity characteristics. In the laser odometer method based on reflectivity characteristics, step S5 includes steps S51 to S52:

[0115] Step S51: converting the target laser point cloud into a two-dimensional laser point map through a coordinate system conversion function.

[0116] In this embodiment, the three-dimensional coordinates of each laser point in the target laser point cloud are converted into two-dimensional coordinates through a coordinate system conversion function to obtain a two-dimensional map of the laser points. Specifically, the calculation is performed in the following way:

[0117]

[0118] Where u and v represent the coordinates of the laser point in the two-dimensional image, x, y, and z represent the three-dimensional coordinates of the laser point, w is the width of the two-dimensional image of the laser point, and h is the height of the two-dimensional image of the laser point. is the depth value of the laser point, and θ is the vertical field of view angle of the laser radar.

[0119] Step S52: According to the initial posture, the two-dimensional laser point image is registered with the photometric feature point map to obtain a second registration result.

[0120] In this embodiment, the photometric feature point map is a two-dimensional map. The two-dimensional coordinates of each point in the laser point two-dimensional map are adjusted according to the initial posture, so that the position state of each point in the laser point two-dimensional map returns to the position state reflected by the current photometric feature point map (that is, the moment when the photometric feature point in the previous frame of laser point cloud is updated to the photometric feature point map), and the laser point two-dimensional map and the photometric feature point map are planarly overlapped to obtain a second alignment result.

[0121] An embodiment of the present application provides a laser odometry method based on reflectivity features, which provides calculation conditions for the laser odometry to construct a reflectivity-based photometric constraint by converting the target laser point cloud into a two-dimensional laser point map and aligning the two-dimensional laser point map with the photometric feature point map according to the initial pose.

[0122] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometer method based on reflectivity characteristics. In the laser odometer method based on reflectivity characteristics, step S6 includes steps S61 to S62:

[0123] Step S61: taking the reflectivity of each point in the two-dimensional laser point image corresponding to the target laser point cloud as a grayscale value, and converting the two-dimensional laser point image into a two-dimensional reflectivity grayscale image.

[0124] In this embodiment, the method for generating a two-dimensional laser point map is as described in step S51, and the grayscale value of each laser point is determined according to the reflectivity of each laser point in the two-dimensional laser point map to generate a grayscale map. The position of each point on the grayscale map corresponds one-to-one to the position of each laser point in the two-dimensional laser point map, and the grayscale value of each point on the grayscale map is consistent with the reflectivity of the laser point in the corresponding two-dimensional laser point map. The grayscale map is a reflectivity grayscale two-dimensional map.

[0125] Step S62: extracting the highest grayscale value points of each local area according to the grayscale gradient of each point in the reflectivity grayscale two-dimensional map, and taking the points in the laser point two-dimensional map corresponding to the highest grayscale value points as the target photometric feature points.

[0126] In this embodiment, by calculating the grayscale gradients of each point in the reflectivity grayscale two-dimensional map in both the horizontal and vertical directions, points whose grayscale gradients in both directions are greater than a preset threshold are screened out. The point screened out by this method is the point with the maximum grayscale in the local area of ​​the reflectivity grayscale two-dimensional map where the point is located. The laser point in the laser point two-dimensional map corresponding to the point is the target photometric feature point with the maximum reflectivity in the local area of ​​the laser point two-dimensional map where the point is located. For example, if the target laser point cloud contains laser points returned by the rectangular plane of a rectangular sign with high reflectivity, then the target laser point cloud is converted into a laser point two-dimensional map, and then converted into a reflectivity grayscale two-dimensional map. By screening the points whose grayscale gradients in both directions in the reflectivity grayscale two-dimensional map are greater than the preset threshold, the target photometric feature points with the maximum reflectivity in the corresponding laser point two-dimensional map are the four vertices of the rectangular plane of the rectangular sign.

[0127] An embodiment of the present application provides a laser odometry method based on reflectivity features. According to the grayscale values ​​of each laser point in the two-dimensional laser point map, a two-dimensional reflectivity grayscale map is generated. By extracting points in the two-dimensional reflectivity grayscale map whose grayscale gradient is greater than a preset threshold, the corresponding target photometric feature points are obtained from the two-dimensional laser point map, thereby providing calculation conditions for the laser odometry to construct a photometric constraint based on reflectivity.

[0128] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometer method based on reflectivity characteristics. In the laser odometer method based on reflectivity characteristics, step S7 includes steps S71 to S75:

[0129] Step S71: constructing a target window of a preset size in the two-dimensional laser point map with the target photometric feature point as the center.

[0130] In this embodiment, the method for obtaining the target photometric feature points is as described in steps S61 and S62. With each obtained target photometric feature point as the center, square target windows of a preset side length are constructed in the two-dimensional laser point map. In addition to the target photometric feature point, the target window also includes several laser points in the two-dimensional laser point map.

[0131] Step S72: Based on the second registration result, a matching window corresponding to the target window is constructed in the photometric feature point map, where the matching window is a window in the photometric feature point map that has the same size and position as the target window.

[0132] In this embodiment, the method for obtaining the second registration result is as described in steps S51 to S52. After the two-dimensional laser point image and the photometric feature point map are planarly overlapped according to the initial pose, matching windows of the same size and position are constructed in the photometric feature point map based on the square target windows constructed in the two-dimensional laser point image.

[0133] Step S73: determining that the reflectivity difference function between the point in the target window and the point in the corresponding matching window is a photometric residual function.

[0134] In this embodiment, after constructing various matching windows of the same size and position in the photometric feature point map based on the various square target windows constructed in the two-dimensional laser point map, the average reflectivity of all laser points contained in the target window is used as the reflectivity value of the target window, and the average reflectivity of all photometric feature points contained in the matching window is used as the reflectivity value of the matching window. The function of calculating the reflectivity difference between the target window and the corresponding matching window is used as the photometric residual function of the target photometric feature points contained in the target window.

[0135] Step S74: constructing a photometric residual matrix according to each photometric residual function.

[0136] In this embodiment, after obtaining the photometric residual function of each target photometric feature point, the reflectivity of the laser point contained in the target window where the corresponding target photometric feature point is located and the reflectivity of the photometric feature point contained in the corresponding matching window are substituted into the function to calculate the photometric residual of each target photometric feature point, and construct a multi-row and single-column photometric residual matrix.

[0137] Step S75: Construct a second Jacobian matrix based on the photometric residual functions, the coordinate system conversion function and the initial pose.

[0138] In this embodiment, the chain rule is used to construct the second Jacobian matrix based on the photometric residual function, the coordinate system conversion function, and the initial pose. Specifically, the calculation is performed as follows:

[0139] in

[0140] Where, is the partial derivative of the photometric residual function with respect to the target window position, is the partial derivative of the target window position with respect to the coordinates of the target photometric feature point, is the partial derivative of the target photometric feature point coordinates with respect to each component in the initial pose, f x and f y is the normalized focal length when generating the two-dimensional image of the laser point.

[0141] An embodiment of the present application provides a laser odometry method based on reflectivity features. A target window is constructed with the target photometric feature point as the center, and a corresponding matching window is constructed in the photometric feature point map. The reflectivity difference function between the point in the target window and the point in the corresponding matching window is used as the photometric residual function. According to each photometric residual function, the coordinate system conversion function and the initial pose, a second Jacobian matrix is ​​constructed, thereby improving the accuracy of constructing the photometric constraint of the laser odometry.

[0142] In combination with the above embodiments, in one embodiment, the present application also provides a laser odometer method based on reflectivity characteristics. In the laser odometer method based on reflectivity characteristics, step S10 includes steps S101 to S103:

[0143] Step S101: Based on the target posture, the target laser point cloud is registered with the geometric feature point map, and new target geometric feature points in the target laser point cloud are determined according to the registration result.

[0144] In this embodiment, the initial pose is iteratively optimized using an iterative error state Kalman filter to obtain the target pose. The target laser point cloud is then re-registered with the geometric feature point map based on the target pose, and new target geometric feature points are re-extracted based on the registration results. The method for re-extracting new target geometric feature points from the target laser point cloud based on the registration results is described in steps S31 to S34 and will not be repeated here.

[0145] Step S102: adding the new target geometric feature point to the geometric feature point map, and adjusting the position of each geometric feature point in the geometric feature point map according to the target posture.

[0146] In this embodiment, after determining the new target geometric feature points, according to the alignment results, the new target geometric feature points located at each coordinate position in the target laser point cloud are added to the same coordinate positions in the geometric feature point map, and the three-dimensional coordinates of the geometric feature point map are adjusted according to the target posture, and the position state of each geometric feature point in the geometric feature point map is updated to the position state corresponding to the moment when the target laser point cloud is collected.

[0147] Step S103: Based on the target posture, the target laser point cloud is aligned with the photometric feature point map, the target photometric feature points are added to the photometric feature point map according to the alignment result, and the position of each photometric feature point in the photometric feature point map is adjusted according to the target posture.

[0148] In this embodiment, the initial pose is iteratively optimized by iterative error state Kalman filtering. After obtaining the target pose, the target laser point cloud and the photometric feature point map are realigned according to the target pose. The method is as described in steps S51 to S52 and will not be repeated here. Based on the alignment results, the target photometric feature points at each coordinate position in the two-dimensional laser point map are added to the same coordinate positions in the photometric feature point map, and the two-dimensional coordinates of the geometric feature point map are adjusted according to the target pose. The position state of each photometric feature point in the photometric feature point map is updated to the position state corresponding to the moment when the target laser point cloud was collected.

[0149] An embodiment of the present application provides a laser odometry method based on reflectivity features. By fusing geometric constraints and photometric constraints, they are jointly used for optimizing the initial pose. The geometric feature points and photometric feature points in the target laser point cloud are added to their corresponding feature point maps according to the optimized target pose. The positions of the feature points in each feature point map are updated in a timely manner according to the target pose, thereby ensuring the positioning accuracy of the laser odometry.

[0150] Based on the same inventive concept, the second aspect of the embodiment of the present application provides a laser odometer system based on reflectivity characteristics, such as Figure 2 As shown, the system includes: a first operation module, a first registration module, a first feature point determination module, a first construction module, a second registration module, a second feature point determination module, a second construction module, a third construction module, a first optimization module, and a first update module;

[0151] The first computing module is configured to obtain an initial posture of the vehicle through an integral operation based on the collected vehicle inertia information;

[0152] The first registration module is used to register the collected target laser point cloud with the geometric feature point map according to the initial posture to obtain a first registration result;

[0153] The first feature point determination module is used to determine a target geometric feature point in the target laser point cloud according to the first registration result, wherein the target geometric feature point is a laser point with a planar feature in the target laser point cloud;

[0154] The first construction module is used to construct a geometric residual matrix and a first Jacobian matrix according to the target geometric feature points, the geometric feature point map and the initial pose;

[0155] The second registration module is used to register the target laser point cloud with the photometric feature point map according to the initial pose to obtain a second registration result;

[0156] The second feature point determination module is used to extract the target photometric feature point with the highest reflectivity in each area of ​​the target laser point cloud;

[0157] The second construction module is used to construct a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose;

[0158] The third construction module is configured to construct a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and to construct a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix;

[0159] The first optimization module is configured to iteratively optimize the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose;

[0160] The first updating module is used to update the geometric feature point map and the photometric feature point map based on the target posture and the target laser point cloud.

[0161] In combination with the above embodiments, in one implementation, the embodiments of the present application further provide a laser odometry system based on reflectivity features. The laser odometry system based on reflectivity features further includes: a first extraction module, a first fitting module, a first determination module, a second determination module, a third determination module, a fourth building module, a fifth building module, a first conversion module, a third registration module, a second conversion module, a second extraction module, a sixth building module, a seventh building module, a fourth determination module, an eighth building module, a ninth building module, a fourth registration module, a first adding module, a fifth registration module, and a second adding module;

[0162] The first extraction module is configured to extract a matching point group corresponding to the laser point in the target laser point cloud from the geometric feature point map according to the first registration result, wherein the matching point group is a plurality of points in the geometric feature point map that are closest to the laser point;

[0163] The first fitting module is configured to fit the points in the matching point group to obtain a corresponding matching plane, and calculate the distance between the points in the matching point group and the matching plane;

[0164] The first determining module is configured to determine a target matching point group among all matching point groups based on the distances between points in each matching point group and their corresponding matching planes, wherein the matching plane corresponding to the target matching point group is the target matching plane;

[0165] The second determining module is used to determine the laser points in the target laser point cloud corresponding to the target matching point group as target geometric feature points;

[0166] The third determining module is used to determine that the distance function from the target geometric feature point to the corresponding target matching plane in the geometric feature point map is a geometric residual function;

[0167] The fourth construction module is used to construct a geometric residual matrix based on each geometric residual function;

[0168] The fifth construction module is used to construct a first Jacobian matrix according to each geometric residual function and the initial pose;

[0169] The first conversion module is used to convert the target laser point cloud into a two-dimensional laser point map through a coordinate system conversion function;

[0170] The third registration module is used to register the two-dimensional laser point image with the photometric feature point map according to the initial posture to obtain a second registration result;

[0171] The second conversion module is configured to use the reflectivity of each point in the two-dimensional laser point image corresponding to the target laser point cloud as a grayscale value, and convert the two-dimensional laser point image into a two-dimensional reflectivity grayscale image;

[0172] The second extraction module is used to extract the highest grayscale value points of each local area according to the grayscale gradient of each point in the reflectance grayscale two-dimensional map, and use the points in the laser point two-dimensional map corresponding to the highest grayscale value points as the target photometric feature points;

[0173] The sixth construction module is used to construct a target window of a preset size in the two-dimensional laser point map with the target photometric feature point as the center;

[0174] The seventh construction module is configured to construct a matching window corresponding to the target window in the photometric feature point map based on the second registration result, wherein the matching window is a window in the photometric feature point map having the same size and position as the target window;

[0175] The fourth determining module is configured to determine that a reflectivity difference function between a point in the target window and a point in the corresponding matching window is a photometric residual function;

[0176] The eighth construction module is used to construct a photometric residual matrix according to each photometric residual function;

[0177] The ninth construction module is used to construct a second Jacobian matrix according to each photometric residual function, the coordinate system conversion function and the initial pose;

[0178] The fourth registration module is used to register the target laser point cloud with the geometric feature point map based on the target posture, and determine new target geometric feature points in the target laser point cloud according to the registration result;

[0179] The first adding module is used to add the new target geometric feature point to the geometric feature point map, and adjust the position of each geometric feature point in the geometric feature point map according to the target posture;

[0180] The second adding module is used to align the target laser point cloud with the photometric feature point map based on the target posture, add the target photometric feature points to the photometric feature point map according to the alignment results, and adjust the position of each photometric feature point in the photometric feature point map according to the target posture.

[0181] Based on the same inventive concept, the third aspect of an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the computer program is executed by the processor, it implements a laser odometer method based on reflectivity features as described in the first aspect of the present application.

[0182] Based on the same inventive concept, the fourth aspect of the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a laser odometer method based on reflectivity features as described in the first aspect of the present application is implemented.

[0183] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0184] It should be noted that for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0185] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0186] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0187] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0188] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0190] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0191] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0192] The above is a detailed introduction to the laser odometer method, system, device and medium based on reflectivity characteristics provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A laser odometer method based on reflectivity characteristics, characterized in that: Applied to a vehicle-mounted laser odometer, the method includes: According to the collected vehicle inertia information, the initial position of the vehicle is obtained through integration operation; According to the initial pose, registering the collected target laser point cloud with the geometric feature point map to obtain a first registration result; Determine, according to the first registration result, a target geometric feature point in the target laser point cloud, wherein the target geometric feature point is a laser point with a planar feature in the target laser point cloud; Construct the geometric residual matrix and the first Jacobian matrix based on the target geometric feature points, geometric feature point map and initial pose; According to the initial pose, registering the target laser point cloud with the photometric feature point map to obtain a second registration result; Extracting target photometric feature points with the highest reflectivity in each area of ​​the target laser point cloud; Constructing a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose; Constructing a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and constructing a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix; Iteratively optimizing the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose; Based on the target pose and target laser point cloud, a geometric feature point map and a photometric feature point map are updated.

2. The laser odometer method based on reflectivity characteristics according to claim 1, characterized in that: Determining target geometric feature points in the target laser point cloud according to the first registration result includes: According to the first registration result, a matching point group corresponding to the laser point in the target laser point cloud is extracted from the geometric feature point map, wherein the matching point group is a plurality of points in the geometric feature point map that are closest to the laser point; Fitting the points in the matching point group to obtain a corresponding matching plane, and calculating the distance between the points in the matching point group and the matching plane; Determine a target matching point group among all matching point groups based on the distances between the points in each matching point group and their corresponding matching planes, and the matching plane corresponding to the target matching point group is the target matching plane; The laser points in the target laser point cloud corresponding to the target matching point group are determined as target geometric feature points.

3. The laser odometer method based on reflectivity characteristics according to claim 2, characterized in that: According to the target geometric feature points, geometric feature point map and initial pose, the geometric residual matrix and the first Jacobian matrix are constructed, including: Determine the distance function from the target geometric feature point to the corresponding target matching plane in the geometric feature point map as the geometric residual function; Based on each geometric residual function, a geometric residual matrix is ​​constructed; Construct the first Jacobian matrix based on each geometric residual function and the initial pose.

4. The laser odometer method based on reflectivity characteristics according to claim 1, characterized in that: According to the initial pose, the target laser point cloud is registered with the photometric feature point map to obtain a second registration result, including: The target laser point cloud is converted into a two-dimensional laser point map through a coordinate system conversion function; According to the initial posture, the two-dimensional laser point image is registered with the photometric feature point map to obtain a second registration result.

5. The laser odometer method based on reflectivity characteristics according to claim 4, characterized in that: Extracting target photometric feature points with the highest reflectivity in each area of ​​the target laser point cloud, including: Taking the reflectivity of each point in the two-dimensional laser point image corresponding to the target laser point cloud as a grayscale value, converting the two-dimensional laser point image into a two-dimensional reflectivity grayscale image; According to the grayscale gradient of each point in the reflectivity grayscale two-dimensional map, the highest grayscale value point of each local area is extracted, and the point in the laser point two-dimensional map corresponding to the highest grayscale value point is used as the target photometric feature point.

6. The laser odometer method based on reflectivity characteristics according to claim 5, characterized in that: According to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose, a photometric residual matrix and a second Jacobian matrix are constructed, including: With the target photometric feature point as the center, a target window of preset size is constructed in the two-dimensional image of the laser point; According to the second registration result, a matching window corresponding to the target window is constructed in the photometric feature point map, wherein the matching window is a window in the photometric feature point map having the same size and position as the target window; Determine a reflectivity difference function between a point in the target window and a point in the corresponding matching window as a photometric residual function; According to each photometric residual function, a photometric residual matrix is ​​constructed; The second Jacobian matrix is ​​constructed based on the photometric residual functions, coordinate system conversion functions and initial poses.

7. The laser odometer method based on reflectivity characteristics according to claim 1, characterized in that: Based on the target pose and the target laser point cloud, a geometric feature point map and a photometric feature point map are updated, including: Based on the target pose, the target laser point cloud is registered with the geometric feature point map, and new target geometric feature points in the target laser point cloud are determined according to the registration result; Adding the new target geometric feature point to the geometric feature point map, and adjusting the position of each geometric feature point in the geometric feature point map according to the target posture; Based on the target posture, the target laser point cloud is aligned with the photometric feature point map, the target photometric feature points are added to the photometric feature point map according to the alignment result, and the position of each photometric feature point in the photometric feature point map is adjusted according to the target posture.

8. A laser odometer system based on reflectivity characteristics, characterized in that: The system comprises: A first calculation module is used to obtain the initial position and posture of the vehicle through integration calculation based on the collected vehicle inertia information; A first registration module is used to register the collected target laser point cloud with the geometric feature point map according to the initial posture to obtain a first registration result; A first feature point determination module is configured to determine a target geometric feature point in the target laser point cloud according to the first registration result, wherein the target geometric feature point is a laser point with a planar feature in the target laser point cloud; A first construction module is used to construct a geometric residual matrix and a first Jacobian matrix according to the target geometric feature points, the geometric feature point map and the initial pose; A second registration module is used to register the target laser point cloud with the photometric feature point map according to the initial pose to obtain a second registration result; A second feature point determination module is used to extract the target photometric feature point with the highest reflectivity in each area of ​​the target laser point cloud; A second construction module is configured to construct a photometric residual matrix and a second Jacobian matrix according to the second registration result, the target photometric feature points, the photometric feature point map and the initial pose; A third construction module is configured to construct a target residual matrix based on the geometric residual matrix and the photometric residual matrix, and to construct a target Jacobian matrix based on the first Jacobian matrix and the second Jacobian matrix; A first optimization module is used to iteratively optimize the initial pose based on the target residual matrix and the target Jacobian matrix to obtain a target pose; The first updating module is used to update the geometric feature point map and the photometric feature point map based on the target posture and the target laser point cloud.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, a laser odometer method based on reflectivity characteristics as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the laser odometer method based on reflectivity features according to any one of claims 1 to 7 is implemented.

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