2d laser positioning quality evaluation method and system, and storage medium

Abstract

The present invention provides a 2D laser positioning quality evaluation method and system, and a storage medium. The method comprises the following steps: on the basis of a planned route and a detection range of a laser radar of a mobile robot, determining an evaluation area and map environment points in the area in a 2D map; calculating point sets of valid reference objects among the map environment points, so as to traverse and calculate feature values and feature vectors of the point sets, and taking a feature vector direction with the maximum feature value as a main direction of the point sets of the valid reference objects; when it is determined that the number of point sets satisfies the number of groups of minimum evaluation angles, grouping all the point sets on the basis of the main direction by using a kmeans algorithm, and then combining the groups having a main direction angle difference smaller than the minimum evaluation angle; otherwise, directly combining the point sets having a main direction angle difference smaller than the minimum evaluation angle to complete grouping; and analyzing the number of groups and lengths of the grouped point sets, and evaluating on the basis of evaluation rules. Therefore, the positioning quality of a planned route in the 2D map can be evaluated without the mobile robot actually running the planned route.

Description

A 2D laser positioning quality assessment method and system, and storage medium Technical Field

[0001] The present invention relates to an autonomous mobile robot positioning analysis technology, and in particular to a 2D laser positioning quality assessment method. Background Art

[0002] The demand for intelligent modern factories is growing, and large-scale warehouses, textile factories, and other facilities are increasingly demanding automated cargo handling capabilities. These factories are typically large and have numerous operating locations, so 2D laser-based navigation solutions are often used in this field. Compared to non-laser navigation solutions like QR codes and magnetic navigation, 2D laser-based navigation solutions require no factory modifications and offer more versatile adaptability, making them the current mainstream navigation solution.

[0003] In real-world scenarios, mobile robots often have complex working paths, and only by ensuring good positioning accuracy along all work paths can the work be completed properly. Due to the large number of work sites in practice, during the construction phase, as shown in Figure 2 of the present invention, a 2D map is first constructed using LiDAR. The mobile robot is then manually controlled to each site, marked in the software, and the routes to each site are then connected according to actual needs. This means that the positioning status of some routes cannot be determined before the operation begins, resulting in inevitably poor positioning accuracy on some routes, which will affect subsequent work.

[0004] To this end, the common practice in the field is to require the mobile robot to actually operate in the field and use a positioning algorithm (such as the widely used particle filter) to calculate the match between the current mobile robot's laser scanning data and the surrounding 2D map to evaluate positioning quality. However, because the current detected map environment is not evaluated, the positioning algorithm often outputs a high confidence level, but the actual environment cannot calculate the accurate position (for example, in a long corridor, the robot can only detect the walls on both sides of the corridor). Therefore, the actual positioning quality in this case is not high. It can be seen that using positioning algorithms to evaluate positioning quality has certain limitations.

[0005] Summary of the Invention

[0006] To this end, the main purpose of the present invention is to provide a 2D laser positioning quality assessment method and system, and a storage medium, so as to evaluate the positioning quality of the planned route in the 2D map without the need for the mobile robot to actually run the planned route, and to solve the limitations of the existing positioning algorithms in the background technology.

[0007] In order to achieve the above object, according to a first aspect of the present invention, a 2D laser positioning quality assessment method is provided, the steps of which include:

[0008] Step S100 determines the evaluation area and map environment points within the area in the 2D map based on the planned path and the detection range of the mobile robot's laser radar;

[0009] Step S200 calculates a point set of valid reference objects in the map environment points, so as to traverse and calculate the eigenvalues ​​and eigenvectors thereof and use the direction of the eigenvector with the largest eigenvalue as the main direction of the point set of valid reference objects;

[0010] Step S300: When it is determined that the number of point sets meets the minimum number of evaluation angle groups, each point set is grouped by main direction using the kmeans algorithm, and then the groups whose main direction angle difference is less than the minimum evaluation angle are merged; otherwise, the point sets whose main direction angle difference is less than the minimum evaluation angle are directly merged to complete the grouping;

[0011] Step S400 analyzes the number and length of the grouped point sets and makes an evaluation based on the evaluation rules.

[0012] In a possible preferred embodiment, the step of determining the map environment point in step 100 includes:

[0013] Step S110 uses a quadtree algorithm to process the 2D map and establish node blocks containing map environment points;

[0014] Step S120 determines the position of the laser radar on the planned path in the 2D map, and uses a ray casting algorithm based on the detection range of the laser radar to determine the map environment points within the node block affected by the ray.

[0015] In a possible preferred embodiment, the 2D laser positioning quality assessment method further comprises the following steps:

[0016] Step S500 marks corresponding positioning evaluation points on the planned path in the 2D map based on the evaluation made in step S400.

[0017] In a possible preferred embodiment, the step of calculating the valid reference object point set in step S200 includes:

[0018] Step S210 generates a binary image from the map environment points;

[0019] In step S220 , relatively discrete adjacent points in the image are excluded according to the discrete threshold value; and a point set that meets the conditions for valid reference objects is located according to the point set cumulative length standard and the point distance standard.

[0020] In a possible preferred embodiment, the minimum number of evaluation angle groups in step S300 is at least 10 groups, and the minimum evaluation angle is 18°.

[0021] In a possible preferred embodiment, the evaluation rules in step S400 include:

[0022] When no valid grouping point set is detected, it is considered abnormal;

[0023] When at least two valid grouping point sets are detected and the lengths of the two valid grouping point sets reach a preset threshold, the current positioning quality is considered good;

[0024] When it is detected that the number of valid grouping point sets is less than two, the current positioning quality is considered to be poor.

[0025] In order to achieve the above object, corresponding to the above method, according to a second aspect of the present invention, a 2D laser positioning quality assessment system is further provided, which includes:

[0026] A storage unit, configured to store a program including any of the steps of the above-described 2D laser positioning quality assessment method, for timely recall and execution by the processing unit and the navigation planning unit;

[0027] The navigation planning unit is used to obtain a 2D map, plan a path, traverse the planned path, and select positioning evaluation points at preset intervals;

[0028] The processing unit is used to determine the evaluation area and the map environment points within the area in the 2D map according to the detection range of the mobile robot's laser radar, so as to calculate the point set of valid reference objects in the map environment points, so as to traverse and calculate their eigenvalues ​​and eigenvectors and use the eigenvector direction with the largest eigenvalue as the main direction of the point set of each valid reference object; then, when the number of point sets is determined to meet the minimum number of evaluation angle groupings, each point set is grouped according to the main direction using the kmeans algorithm, and then the groups whose main direction angle difference is less than the minimum evaluation angle are merged; otherwise, the point sets whose main direction angle difference is less than the minimum evaluation angle are directly merged to complete the grouping; then, the number and length of the point sets after grouping are analyzed, and an evaluation is made according to the evaluation rules.

[0029] In a possible preferred embodiment, the navigation planning unit is further configured to mark corresponding positioning evaluation points on the planned path in the 2D map according to the evaluation made.

[0030] In a possible preferred embodiment, the processing unit uses a quadtree algorithm to process the 2D map, establishes node blocks containing map environment points, and uses a ray casting algorithm at the positioning evaluation point based on the detection range of the lidar to determine the map environment points within the node blocks affected by the ray.

[0031] In order to achieve the above-mentioned purpose, corresponding to the above-mentioned method, according to the third aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the 2D laser positioning quality assessment method as described in any of the above items are implemented.

[0032] The 2D laser positioning quality assessment method, system, and storage medium provided by the present invention cleverly break away from the limitations of traditional positioning algorithms in estimating positioning quality, and instead transform the 2D laser-based positioning quality assessment problem into a clustering and feature analysis problem on the image. This not only reduces the computational complexity and computing power requirements, but also solves the limitations of traditional positioning algorithm solutions. More importantly, this solution can evaluate the positioning quality on the planned path in a 2D map without the actual operation of the mobile robot, thereby greatly saving the time and cost required for the evaluation. This helps to point out unreasonable planned routes in advance and helps on-site personnel quickly deploy operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0034] FIG1 is a schematic diagram of the steps of a 2D laser positioning quality assessment method according to the present invention;

[0035] FIG2 is a schematic diagram of the mobile robot's approach to the work site and planned path manually marked in the existing 2D laser map;

[0036] FIG3 is a schematic diagram of a map area that a conventional mobile robot can perceive based on the sensing range of its laser radar;

[0037] FIG4 is a schematic diagram of the morphology of a 2D map after quadtree processing in the 2D laser positioning quality assessment method of the present invention;

[0038] 5 to 8 are schematic diagrams of a 2D laser positioning quality assessment method according to the present invention, in which a ray casting algorithm is used to gradually identify map environment points illuminated by a laser radar in a quadtree map;

[0039] FIG9 is a schematic diagram illustrating the radiation area of ​​the ray casting algorithm and the projected map environment points in the 2D laser positioning quality assessment method of the present invention;

[0040] FIG10 is a schematic diagram of converting map environment points into a binary image in the 2D laser positioning quality assessment method of the present invention;

[0041] FIG11 is a schematic diagram showing relatively discrete map environment points in the 2D laser positioning quality assessment method of the present invention;

[0042] FIG12 is a schematic diagram showing relatively dense map environment points in the 2D laser positioning quality assessment method of the present invention;

[0043] FIG13 is a schematic diagram showing a valid reference object point set and main directions in the 2D laser positioning quality assessment method of the present invention;

[0044] FIG14 is a schematic diagram showing a mobile robot in a corridor environment in the 2D laser positioning quality assessment method of the present invention;

[0045] FIG15 is a schematic diagram showing a corridor environment and a corner environment in a 2D map in the 2D laser positioning quality assessment method of the present invention;

[0046] FIG16 is a schematic diagram showing the number and grouping of point sets in the 2D laser positioning quality assessment method of the present invention;

[0047] FIG17 is a schematic diagram of a planned path in a 2D map after marking the positioning quality assessment results of the positioning points in the 2D laser positioning quality assessment method of the present invention;

[0048] FIG18 is a schematic diagram of the structure of the 2D laser positioning quality assessment system of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the specific technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments to help those skilled in the art further understand the present invention. Obviously, the embodiments described in this case are only embodiments of a part of the present invention, rather than all embodiments. It should be pointed out that, for those of ordinary skill in the art, the embodiments in this application and the features in the embodiments can be combined with each other without departing from the concept of the present invention and without conflicting with each other. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work should fall within the disclosure and protection scope of the present invention.

[0050] In addition, the terms "first", "second", "S1", "S2", etc. in the specification, claims and drawings of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those described herein. At the same time, the terms "including" and "having" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. Unless otherwise expressly specified and limited, the terms "setting", "layout", "installation", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a communication between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in this case can be understood based on the specific circumstances and in combination with the existing technology.

[0051] The operating principle of a single-line lidar is that the transmitter rotates at a constant speed inside the lidar, emitting laser light once for every small rotation angle (called angular resolution). After patrolling a certain angle, a complete frame of data is generated. However, this also means that a single-line lidar can only recognize a row of dots at the same height and can only describe linear information, not surfaces or heights. Figure 2 shows a 2D map constructed using a single-line lidar.

[0052] As shown in Figure 2, the rectangular boxes in the 2D map represent stations, indicating the locations to which the mobile robot must move during operation. The lines connecting the stations represent the manually planned paths. The paths can be straight or curved, and the mobile robot will follow them during operation. Traditionally, positioning quality assessment requires the mobile robot to actually run through these stations and the planned paths, thus enabling ongoing evaluation of positioning quality.

[0053] Therefore, in order to evaluate the positioning quality of a planned route in a 2D map without actually moving the robot to run the planned route, the present invention provides a 2D laser positioning quality evaluation method as shown in Figures 1 to 15, which includes the following steps:

[0054] Step S100 determines the evaluation area and map environment points within the area in the 2D map according to the planned path and the detection range of the mobile robot's laser radar.

[0055] Specifically, as shown in Figure 3, this example scenario involves a mobile robot using only a single laser line to determine its current location based on a pre-built map. The sensing area of ​​the LiDAR installed on the mobile robot is shown in Figure 3. This laser sensing area is affected by the laser's detection range (typically 20-40 meters) and its detection angle range (typically 180-360°). Therefore, the LiDAR's detection range must be determined in advance for accurate assessment.

[0056] At the same time, in order to evaluate the positioning quality of any point on the planned route, it is also necessary to determine the evaluation area that can be scanned in the 2D map based on the detection range of the laser radar to obtain the map environment points that the laser can detect at that location. Assume that the laser is installed at the position on the mobile robot. The transformation matrix from the laser coordinate system to the vehicle coordinate system is known. Given the position of the vehicle in the map You can calculate the position of the laser on the map That is, locate the evaluation point, traverse the planned path at the same time, select the evaluation point for evaluation every preset distance, and the planned path can be evaluated throughout.

[0057] Furthermore, in order to determine the evaluation area and the map environment points within the area in the 2D map, the steps of this example include:

[0058] Step S110 uses a quadtree algorithm to process the 2D map and establish node blocks containing map environment points.

[0059] Specifically, as shown in Figure 4, the process of generating a quadtree map is illustrated. First, the X and Y axis ranges of the current map point are calculated, with width = x_max – x_min and height = y_max – y_min. The root node created is the larger of width and height, as shown in the outermost box (a square) in Figure 4. The width and height of the root node are then divided equally to generate four child nodes (child nodes also generate their own four child nodes in the same way). Since the root node calculates the range of the entire map, every point is included.

[0060] Then add each point to the quadtree one by one. The process is: determine which child node of the root node (one of the four directions) the current point is located in based on its coordinates. Determine whether the current child node contains its own child nodes. If it does not have its own child nodes, generate its own child nodes. After generation, it is necessary to determine whether the current width is the minimum width (the minimum width is usually the resolution of the map, such as 1mm). If it is larger than the minimum width, continue to determine which child node the current point is located in. If it is empty, continue to generate child nodes until the width of the child node is the minimum width. The current point is located in the current child node.

[0061] If it has its own child node, continue to determine which child node the current point is in. If it is not empty, continue to determine which child node it is in until the width of the child node reaches the minimum width. The current point is located in the current child node, thereby generating a quadtree map to determine the node block containing the map environment point.

[0062] Step S120 determines the position of the laser radar on the planned path in the 2D map, and uses a ray-casting algorithm based on the detection range of the laser radar to determine the map environment points within the node block affected by the ray.

[0063] Specifically, as shown in Figures 5 to 8, this step generates a map of environmental points detected by the laser at a specified location. First, the smallest child node (width equal to the minimum resolution) of the quadtree where the current laser position falls is found. This point is the laser emission point of the laser model. Based on the angular resolution and angular range of the current laser parameters, starting from the minimum angle, the laser is judged in sequence to determine whether the laser emitted at that angle falls on the environmental point.

[0064] As shown in Figure 6, assuming the current laser position is the starting point of the arrow, the child node located at the dead cross is shown in the upper box. This child node also has two non-empty nodes and two empty child nodes. The direction of the arrow represents the current laser emission angle θ. In this case, the angle is negative. Calculate the minimum y value of the empty child node A to which the laser position belongs, subtract 0.01 (to jump out of the current child node's range), and get y_1, the value indicated by the bold line in Figure 6. Based on the linear equation y = tanθ*x+c, calculate the corresponding x value: x_1 = (y_1-c) / tanθ. Then determine whether x_1 is within the x range of child node A. If so, there are two possible cases: If the angle is less than -90 degrees (which should be outside the right of the current child node), set x_2 = the minimum x value of child node A – 0.01. If it is greater than -90 degrees (as in the figure below), set x_2 = the maximum x value of child node A. Then, calculate the corresponding y value based on the linear equation: y_2 = tanθ*x_2+c. At this time, (x_2, y_2) is at the circle mark in Figure 6, and its child node is the empty child node B in the child nodes shown in the lower box in Figure 6 (containing 3 empty child nodes and one non-empty child node).

[0065] Then, similarly, find the minimum y value of child node B, subtract 0.01 (the purpose is to jump out of the current child node range) to get y_3, as shown by the bold line in Figure 7. According to the linear equation y = tanθ*x+c, calculate the x value corresponding to this y value: x_3 = (y_3-c) / tanθ. Determine whether x_3 is within the x range of child node B. In this case, it is not (the case of being within has been explained above for reference). Calculate the child node to which (x_3, y_3) belongs, such as the empty child node C in the upper left corner of the child node in the lower box in Figure 7 (circled in Figure 7).

[0066] According to the same logic as above, it can be calculated that the point on the straight line hits the environmental point at the small inner box in Figure 8.

[0067] The above example is for a ray angle less than 0. If the ray angle is greater than 0, the maximum y value is used to obtain y_1. The corresponding x value for this y value is calculated using the line equation y = tanθ*x+c: x_1 = (y_1-c) / tanθ. Then, we determine whether x_1 is within the x range of child node A. If so, we can consider two cases: If the angle is not less than 90 degrees, we set x_2 = the minimum x value of child node A – 0.01. If the angle is less than 90 degrees, we set x_2 = the maximum x value of child node A. Then, we calculate the corresponding y value using the line equation: y_2 = tanθ*x_2+c. The rest of the calculation is the same as in the above example.

[0068] In addition, there are two other cases. First, calculate the farthest laser detection distance point (org_x2, org_y2). Assuming that the laser emission point is (org_x1, org_y1), then:

[0069] org_x2=org_x1+cosθ*laser detection distance;

[0070] org_y2=org_y1+sinθ*laser detection distance;

[0071] Case 1, horizontal line

[0072] If (org_x1>org_x2) x_2 = minimum x value of child node A – 0.01, y_2 = org_y1

[0073] else x_2=the maximum value of x of child node A, y_2=org_y1

[0074] Then calculate whether the child node of (x_2, y_2) has reached the minimum resolution. If it has reached the minimum resolution, the node is the environmental point hit by the laser. If not, continue to perform the above calculation.

[0075] Case 2, vertical line

[0076] If (org_y1>org_y2) y_2 = minimum y value of child node A – 0.01, x_2 = org_x1

[0077] else y_2=the maximum value of y of child node A, x_2=org_x1

[0078] Then calculate whether the child node of (x_2, y_2) has reached the minimum resolution. If it has reached the minimum resolution, the node is the environmental point hit by the laser. If not, continue to perform the above calculation.

[0079] Finally, as shown in Figure 9, the points on the map that can be illuminated by the current position of the laser are determined by using the Ray-casting algorithm. These points are a collection of two-dimensional coordinates:

[0080] In order to visually display the traces of these points in the figure, these points are connected to the laser radar respectively, and a ray diagram as shown in Figure 9 can be obtained to illustrate the expression form of the above algorithm.

[0081] Step S200 calculates the point set of valid reference objects in the map environment point, so as to traverse and calculate the eigenvalues ​​and eigenvectors thereof and use the eigenvector direction with the largest eigenvalue as the main direction of the point set of each valid reference object.

[0082] Specifically, after obtaining the map environment points that can be illuminated by the lidar, in order to further evaluate the positioning quality, this example cleverly breaks away from the thinking limitations of traditional positioning algorithms that estimate positioning quality, and instead transforms the 2D laser-based positioning quality assessment problem into an image clustering and feature analysis problem.

[0083] To this end, in this example, the step of calculating the valid reference object point set in step S200 includes:

[0084] As shown in FIG10 , step S210 generates a binary image from the map environment points.

[0085] For example, calculate the maximum and minimum values ​​of (x, y) in the above S to obtain laser_y_max, laser_y_min, laser x max, laser x min.

[0086] According to the resolution m, calculate the length and width pixel values ​​of the image:

[0087] Image height = (laser_y_max - laser_y_min) / m;

[0088] Image width = (laser_x_max - laser_xmin) / m;

[0089] Traverse each point p in S, calculate the pixel coordinates corresponding to each point, and set the value of the pixel to 1;

[0090] Row coordinate = image height - (py-laser_y_min) / m;

[0091] Column coordinate = (px - laser_x_min) / m.

[0092] The set of these image points is These points are arranged in the order of laser scanning.

[0093] In step S220 , relatively discrete adjacent points in the image are excluded according to the discrete threshold value; and a point set that meets the conditions for valid reference objects is located according to the point set cumulative length standard and the point distance standard.

[0094] Specifically, as shown in Figure 11, in a binary image, if adjacent points are relatively discrete (such as the points within the box in Figure 11), these points clearly do not belong to the same environmental object and cannot provide an effective reference for positioning and can be eliminated. Similarly, if there are many adjacent points (such as those indicated by the arrows in Figure 12 below), such a point set often belongs to a larger environmental reference object and can provide an effective reference for positioning.

[0095] In order to determine the valid reference point set. In this example, the example defines that the valid reference point set needs to meet the requirement that the cumulative length of the point set is not less than 0.5 meters and the distance between two points is not greater than 0.1 meters. , thereby obtaining the point set of all valid reference objects.

[0096] Next, we traverse the valid reference point sets one by one, using the OpenCV library cv::PCA function to find the eigenvalues ​​and eigenvectors of these point sets. The direction of the eigenvector with the largest eigenvalue in each point set is taken as the principal direction of the valid reference object. As shown in Figure 13, we can ultimately determine the 11 valid reference point sets and their principal directions in the binary image (circled in Figure 13).

[0097] In step S300, when it is determined that the number of point sets meets the minimum number of evaluation angle groups, each point set is grouped according to the main direction using the kmeans algorithm, and then the groups whose main direction angle difference is less than the minimum evaluation angle are merged; otherwise, the point sets whose main direction angle difference is less than the minimum evaluation angle are directly merged to complete the grouping.

[0098] Specifically, as shown in Figure 14, the inventors observed that when a mobile robot is walking in a long corridor, if the most commonly used particle filter positioning algorithm is used, assuming that the pose is calculated at position 1, the algorithm will scatter many particles at different locations near the current position. After calculation, the particles in the x-direction all have high scores because the detected environment closely matches the map (both are two parallel straight lines), but the robot's true position is only one. The final pose is averaged according to the weights of all particle positions (the higher the score, the higher the weight). In this case, the averaged Y position is accurate, but in the x-direction, there are many particles with high weights at different x positions, resulting in an average value between these x values, making it impossible to calculate the correct x value. When scoring this position again, the laser-detected environment also matches the map, so the score will be high, but in reality, the confidence level of the position is very low.

[0099] In fact, the inventor believes that any positioning algorithm, at its essence, is the process of aligning the data detected by the current laser with the map data. Therefore, the conception of this case believes that for 2D lasers, the requirement for alignment is that the laser must detect at least two areas with different orientations, and the lengths of the two areas must be long enough to ensure that the positioning algorithm can correctly solve the current position. The purpose of abstract analysis of the environment (i.e., the point set of valid reference objects detected by the laser in the surrounding environment in this case) is precisely to verify whether this condition holds. In this way, compared with using direct mathematical algorithms, the use of abstract analysis can obtain accurate quality assessment solutions.

[0100] Therefore, in practice, if the laser radar detects two parallel walls on the map terrain shown on the left side of Figure 15, the positioning quality is poor. However, if the laser radar can detect two areas with different orientations and the length of the two areas is long enough, it can effectively estimate the current position, as shown on the map terrain on the right side of Figure 15, and the positioning quality is good.

[0101] Furthermore, according to the inventors' theory, to identify objects with different orientations, the detected valid reference point set needs to be divided into several groups, and the minimum evaluation angle between each group must be no less than 18°. The 18° value here is an empirical value that works well in practice and is intuitively sufficient to distinguish different orientations. However, those skilled in the art can adjust it appropriately based on actual environmental conditions, so this example does not limit it.

[0102] Taking the minimum evaluation angle of 18° as an example, the actual number of groups can be 10 groups (i.e., 180° / 18°). Each point set is divided into 10 groups according to the main direction using the kmeans algorithm. In fact, this is to ensure that the worst case is one group with 18°. If the current number of point sets is greater than 10 groups, such as 11 groups in the above example, the kmeans algorithm can be used for grouping, and then the groups with a main direction angle difference of less than 18° can be merged. Finally, as shown in Figure 16, a total of 5 groups 0-4 can be obtained; conversely, if the current number of point sets is less than 10 groups, the point sets with a main direction angle difference of less than 18° can be directly merged to complete the grouping. At this point, the number of valid directions detected (i.e., the number of groups) can be evaluated.

[0103] Step S400 analyzes the number and length of the grouped point sets and makes an evaluation based on the evaluation rules.

[0104] Specifically, based on the above-mentioned theories of the inventors and their practical experience, it is necessary to perform analysis based on the aforementioned grouping situations and the characteristics of each group. In this example, it is preferred to set the evaluation rules to three corresponding situations:

[0105] 1) When no valid grouping point set is detected, it is considered abnormal.

[0106] 2) When at least two valid grouping point sets are detected (considered as at least two directions being detected), and the length of the two valid grouping point sets reaches a preset threshold (such as greater than 3 meters), the current positioning quality is considered to be good.

[0107] 3) When the number of valid grouping point sets detected is less than two, the current positioning quality is considered to be poor.

[0108] In addition, in order to further subdivide the situation of poor positioning quality, it can also be divided into:

[0109] 3.1) If there is only one group (one direction) and its length is less than 1 meter, the current positioning quality is considered extremely poor. This means that the recommendation is to provide more effective detection areas in different directions. This can guide the path planning solution to adjust.

[0110] 3.2) If there is only one group (in one direction) and its length is greater than 1 meter, the current positioning quality is considered to be very poor.

[0111] 3.3) If there is at least one group and the longest group is less than 3 meters long, the current positioning quality is considered to be very poor.

[0112] 3.4) If there is at least one group, and the sum of the lengths of all groups except the longest group is less than 3 meters, the current positioning quality is considered to be poor.

[0113] Therefore, the quality evaluations in 3.1-3.2 above suggest that more effective detection areas in different directions are needed. The quality evaluations in 3.3-3.4 suggest, respectively, that more effective environmental reference objects be provided in the primary direction and more effective environmental reference objects be provided in non-primary directions. This will guide adjustments to the path planning solution.

[0114] In addition, the length threshold in the above examples can be adjusted according to the actual map environment, and the present invention does not impose any limitation thereto. Therefore, any adaptive adjustment made based on the inventive concept of the above examples of the present invention is within the scope of the present invention.

[0115] Furthermore, in order to intuitively understand the positioning quality assessment of the planned path on the 2D map, in a preferred example, as shown in FIG17 , the method example further includes:

[0116] Step S500 marks the corresponding positioning evaluation points on the planned path on the 2D map based on the evaluation results of step S400. The marks can correspond to different evaluation results in step S400, providing different identification, such as using different colors. When each positioning evaluation point is marked, the positioning quality at each location on the planned path can be intuitively displayed. For example, the area indicated by the box in Figure 17 indicates poor positioning quality, which suggests adjustments.

[0117] This example of a 2D laser positioning quality assessment method allows for the evaluation of positioning quality along a planned path within a 2D map, significantly reducing the time and cost required for this assessment. This helps identify unreasonable planned routes in advance, enabling on-site personnel to quickly deploy operations.

[0118] On the other hand, corresponding to the above method example, as shown in FIG18 , the present invention further provides a 2D laser positioning quality assessment system, which includes:

[0119] A storage unit, configured to store a program including any of the steps of the 2D laser positioning quality assessment method described in the above examples, for timely recall and execution by the processing unit and the navigation planning unit;

[0120] The navigation planning unit is used to obtain a 2D map, plan a path, traverse the planned path, and select positioning evaluation points at preset intervals;

[0121] The processing unit is used to determine the evaluation area and the map environment points within the area in the 2D map according to the detection range of the mobile robot's laser radar, so as to calculate the point set of valid reference objects in the map environment points, so as to traverse and calculate their eigenvalues ​​and eigenvectors and use the eigenvector direction with the largest eigenvalue as the main direction of the point set of each valid reference object; then, when the number of point sets is determined to meet the minimum number of evaluation angle groupings, each point set is grouped according to the main direction using the kmeans algorithm, and then the groups whose main direction angle difference is less than the minimum evaluation angle are merged; otherwise, the point sets whose main direction angle difference is less than the minimum evaluation angle are directly merged to complete the grouping; then, the number and length of the point sets after grouping are analyzed, and an evaluation is made according to the evaluation rules.

[0122] Furthermore, the navigation planning unit is further configured to mark corresponding positioning evaluation points on the planned path in the 2D map according to the evaluation made.

[0123] Furthermore, the processing unit uses a quadtree algorithm to process the 2D map, establishes a node block containing map environment points, and uses a ray casting algorithm at the positioning evaluation point based on the detection range of the lidar to determine the map environment points within the node block affected by the ray.

[0124] On the other hand, corresponding to the above method examples, the present invention also provides a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the 2D laser positioning quality assessment method as described in any of the above examples are implemented.

[0125] In summary, the 2D laser positioning quality assessment method, system, and storage medium provided by the present invention cleverly break away from the limitations of traditional positioning algorithms in estimating positioning quality, and instead transform the 2D laser-based positioning quality assessment problem into a clustering and feature analysis problem on the image. This not only reduces the computational complexity and computing power requirements, but also solves the limitations of traditional positioning algorithm solutions. More importantly, this solution can evaluate the positioning quality on the planned path in a 2D map without the actual operation of the mobile robot, thereby greatly saving the time and cost required for the evaluation. This helps to point out unreasonable planned routes in advance and helps on-site personnel quickly deploy operations.

[0126] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made based on the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0127] Those skilled in the art will understand that, in addition to implementing the system, device, and various modules provided by the present invention in pure computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by logically programming the method steps. Therefore, the system, device, and various modules provided by the present invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0128] In addition, all or part of the steps in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a single-chip microcomputer, chip or processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code.

[0129] In addition, various implementations of the embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A 2D laser positioning quality assessment method, the steps comprising: Step S100 determines the evaluation area and map environment points within the area in the 2D map according to the planned path and the detection range of the mobile robot laser radar; Step S200 calculates a point set of valid reference objects in the map environment points, so as to traverse and calculate its eigenvalues ​​and eigenvectors and use the direction of the eigenvector with the largest eigenvalue as the main direction of the point set of valid reference objects; Step S300: When it is determined that the number of point sets meets the minimum number of evaluation angle groups, each point set is grouped by main direction using the kmeans algorithm, and then the groups whose main direction angle difference is less than the minimum evaluation angle are merged; otherwise, the point sets whose main direction angle difference is less than the minimum evaluation angle are directly merged to complete the grouping; Step S400 analyzes the number and length of the grouped point sets and makes an evaluation based on the evaluation rules.

2. The 2D laser positioning quality assessment method according to claim 1, wherein the step of determining the map environment point in step 100 comprises: Step S110 uses a quadtree algorithm to process the 2D map and establish node blocks containing map environment points; Step S120 determines the position of the laser radar on the planned path in the 2D map, and uses a ray casting algorithm based on the detection range of the laser radar to determine the map environment points within the node block affected by the ray.

3. The 2D laser positioning quality assessment method according to claim 1, wherein the steps further include: Step S500 marks the corresponding positioning evaluation points on the planned path in the 2D map according to the evaluation made in step S400.

4. The 2D laser positioning quality assessment method according to claim 1, wherein the step of calculating the valid reference object point set in step S200 comprises: Step S210 generates a binary image from the map environment points; Step S220 excludes relatively discrete adjacent points in the image according to the discrete threshold; locates a point set of valid reference objects that meet the conditions according to the point set cumulative length standard and the point distance standard.

5. The 2D laser positioning quality assessment method according to claim 1, wherein the minimum assessment angle grouping number in step S300 is at least 10 groups, and the minimum assessment angle is 18°.

6. The 2D laser positioning quality assessment method according to claim 1, wherein the assessment rule in step S400 comprises: When no valid grouping point set is detected, it is considered abnormal; When at least two valid grouping point sets are detected and the lengths of the two valid grouping point sets reach a preset threshold, the current positioning quality is considered good; When it is detected that the number of valid grouping point sets is less than two, it is considered that the current positioning quality is poor.

7. A 2D laser positioning quality assessment system, comprising: A storage unit, used to store a program including the steps of the 2D laser positioning quality assessment method as described in any one of claims 1 to 5, so that the processing unit and the navigation planning unit can call and execute it in a timely manner; The navigation planning unit is used to obtain a 2D map, plan a path, traverse the planned path, and select positioning evaluation points at preset distances; The processing unit is used to determine the evaluation area and the map environment points in the area in the 2D map according to the detection range of the mobile robot laser radar, so as to calculate the point set of the effective reference object in the map environment point, so as to traverse and calculate its eigenvalue and eigenvector and use the eigenvector direction with the largest eigenvalue as the main direction of the point set of the effective reference object; then, when the number of point sets is determined to meet the minimum number of evaluation angle groups, each point set is grouped according to the main direction using the kmeans algorithm, and then the groups with the main direction angle difference less than the minimum evaluation angle are merged; otherwise, the point sets with the main direction angle difference less than the minimum evaluation angle are directly merged to complete the grouping; then, the number and length of the point sets after grouping are analyzed, and evaluation is made according to the evaluation rules.

8. The 2D laser positioning quality assessment system according to claim 7, wherein the navigation planning unit is further used to mark corresponding positioning assessment points on the planned path in the 2D map according to the evaluation made.

9. A 2D laser positioning quality assessment system according to any one of claims 7 or 8, wherein the processing unit uses a quadtree algorithm to process a 2D map, establishes node blocks containing map environment points, and uses a ray casting algorithm at the positioning assessment point based on the detection range of the laser radar to determine the map environment points within the node blocks affected by the ray.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the 2D laser positioning quality assessment method according to any one of claims 1 to 5 are implemented.

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