Ground point cloud point determination method and device based on laser radar

Abstract

The invention belongs to the field of machine vision, and provides a ground point cloud point determination method and device based on a laser radar and a storage medium, and the method comprises the steps: dividing all to-be-determined point cloud points into a plurality of cylinder grids according to coordinate positions, the to-be-determined point cloud point is a point cloud point which is collected by a laser radar and is in a ground point detection range, and all the columnar grids are combined to form the ground point detection range. And determining a reserved cylinder grid according to the height value of each point cloud point in each cylinder grid, and determining all to-be-determined point cloud points in the reserved cylinder grid as ground point cloud points. By means of the scheme, the problems that the false detection rate is high, and the non-target point filtering effect is poor are solved.

Description

Technical Field

[0001] This application belongs to the field of machine vision, and particularly relates to a method and device for determining ground point cloud points based on lidar. Background Art

[0002] Lidar measures the distance and angle from the scanner to an object through rapidly emitted laser pulses, and accurately and quickly obtains the three-dimensional data of the object surface. It is an important means for information acquisition. The data obtained by scanning is discrete three-dimensional points, called point cloud.

[0003] In the vehicle-road collaborative scenario, the ground point filtering algorithm for lidar point cloud is a widespread and important technology. Currently, mainstream object detection algorithms can only set fixed thresholds to filter ground points when processing raw point cloud data. Fixed thresholds are prone to filtering out target points, resulting in a relatively high false detection rate and poor filtering effect for non-target points in this situation. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for determining ground point cloud points based on lidar, which can reduce the false detection rate and improve the filtering effect of non-target points.

[0005] In a first aspect, an embodiment of this application provides a method for determining ground point cloud points based on lidar, and the method includes:

[0006] Dividing all the point cloud points to be determined into multiple cylindrical grids according to the coordinate positions, where the point cloud points to be determined are the point cloud points collected by the lidar within the ground point detection range, and all the cylindrical grids together form the ground point detection range;

[0007] Determine the retained cylindrical grids according to the height values of each point cloud point in each cylindrical grid, and determine all the point cloud points to be determined in the retained cylindrical grids as ground point cloud points.

[0008] In an implementable embodiment, the method further includes:

[0009] Screen out the point cloud point spatial dataset within the ground point detection range from the point cloud data collected by the lidar.

[0010] In an implementable embodiment, the determining the ground point cloud points in each cylindrical grid according to the height values of each point cloud point in each cylindrical grid includes:

[0011] Determine whether each cylindrical grid is retained according to the height value of the lowest ground point corresponding to each cylindrical grid and the height values of all the point cloud points in each cylindrical grid.

[0012] In an implementable embodiment, determining the ground point cloud points in each column grid according to the height values of each point cloud point in each column grid further includes:

[0013] Sort all the point cloud points in each column grid according to the height values, and determine the to-be-determined point cloud point with the lowest height value;

[0014] According to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each column grid and the height values of all the point cloud points in each column grid, determine whether each column grid is retained.

[0015] In an implementable embodiment, determining the ground point cloud points in each column grid according to the height values of each point cloud point in each column grid further includes:

[0016] For each column grid, repeatedly compare the height values of any two point cloud points, and compare the point cloud point with the lower height value with any other point cloud point until all the point cloud points are compared. Take the point cloud point with the lower height value obtained in the last comparison as the to-be-determined point cloud point with the lowest height value;

[0017] According to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each column grid and the height values of all the point cloud points in each column grid, determine whether each column grid is retained.

[0018] In an implementable embodiment, determining whether each column grid is retained according to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each column grid and the height values of all the point cloud points in each column grid includes:

[0019] For each column grid, according to the difference between the height value of the to-be-determined point cloud point with the lowest height value and the height values of each to-be-determined point cloud point in the column grid, and in combination with a preset target height distribution range, determine the number of qualified point cloud points in each column grid;

[0020] According to the number of qualified point cloud points in each column grid, and in combination with a preset filtering threshold parameter, determine whether each column grid is retained.

[0021] In an implementable embodiment, determining the number of qualified point cloud points in each column grid according to the difference between the height value of the to-be-determined point cloud point with the lowest height value and the height values of each to-be-determined point cloud point in the column grid, and in combination with a preset target height distribution range includes:

[0022] For each to-be-determined point cloud point, determine whether the difference is within the target height distribution range;

[0023] If so, take the corresponding to-be-determined point cloud point as a qualified point cloud point;

[0024] Calculate the number of qualified point cloud points in each column grid.

[0025] In a feasible embodiment, determining whether to retain each column grid according to the number of qualified point cloud points in each column grid and in combination with a preset filtering threshold parameter includes:

[0026] Judge whether the number of qualified point cloud points corresponding to each column grid is higher than the filtering threshold parameter. If it is higher, determine to retain the column grid.

[0027] In a second aspect, an embodiment of the present application provides a ground point cloud point determination device based on a lidar, including:

[0028] A preprocessing unit for dividing all point cloud points to be determined into multiple column grids according to coordinate positions. The point cloud points to be determined are the point cloud points collected by the lidar within the ground point detection range, and all the column grids form the ground point detection range;

[0029] A processing unit for determining the retained column grids according to the height values of each point cloud point in each column grid, and determining all the point cloud points to be determined in the retained column grids as ground point cloud points.

[0030] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the method as described in any one of the above.

[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by an electronic device, the method as described in any one of the above is implemented.

[0032] The beneficial effects of the embodiments of the present application compared with the prior art are:

[0033] By dividing all point cloud points to be determined into multiple column grids according to coordinate positions, determining the retained column grids according to the height values of each point cloud point in each column grid, and determining all the point cloud points to be determined in the retained column grids as ground point cloud points to filter the ground points, the false detection rate of ground point filtering is effectively reduced, and the filtering effect of non-target points is improved. Description of the Drawings

[0034] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0035] Figure 1 It is an application scenario diagram of a method for determining ground point cloud points based on lidar provided by the present application;

[0036] Figure 2 It is a schematic flowchart of a method for determining ground point cloud points based on lidar provided by the present application;

[0037] Figure 3 It is a schematic flowchart of a method for dividing columnar grids in an embodiment provided by the present application;

[0038] Figure 4 It is a schematic flowchart of a method for determining the lowest ground points in an embodiment provided by the present application;

[0039] Figure 5 It is a schematic flowchart of a method for determining the lowest ground points in another embodiment provided by the present application;

[0040] Figure 6 It is a schematic flowchart of a method for determining the lowest ground points in another embodiment provided by the present application;

[0041] Figure 7 It is a schematic flowchart of a method for retaining columnar grids in an embodiment provided by the present application;

[0042] Figure 8 It is a schematic flowchart of an embodiment of a method for determining ground point cloud points based on lidar provided by the present application;

[0043] Figure 9 It is a schematic diagram of an embodiment scenario of a method for determining ground point cloud points based on lidar provided by the present application;

[0044] Figure 10 It is a schematic diagram of the structure of a device for determining ground point cloud points based on lidar provided by the present application;

[0045] Figure 11 It is a schematic diagram of the structure of an electronic device provided by the present application. Detailed implementation manners

[0046] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures, technologies, etc. are provided to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of the present application.

[0047] It should be understood that when used in the specification and claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0048] It should also be understood that the term "and / or" as used in the specification and claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0049] As used in the specification and claims of the present application, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0050] In addition, in the description of the specification and claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0051] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0052] It should be understood that the sequence numbers of the steps in this embodiment do not indicate the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0053] At present, the intelligentization of automobiles, as a major trend of the development of the times, has always been the focus of attention of many industries such as the Internet and automobile enterprises. As one of the core technologies in the field of intelligent driving, environmental perception is the only way for automobiles to achieve intelligentization. At present, for intelligent automobiles, such as vehicles of the type of intelligent logistics vehicles, one way to obtain external environmental information is to use sensors such as cameras. Although cameras and other sensors are inexpensive, there are problems such as low measurement accuracy of the distance to obstacles and weak perception ability of the three-dimensional information of obstacles. Another relatively better way is to use a low-beam lidar to obtain external environmental information. However, due to the relatively low sampling accuracy of the low-beam lidar, the recognition of obstacles is not accurate enough. Therefore, how to improve the detection and filtering of ground point clouds is an urgent problem to be solved. Figure 1 For the scenario diagram of the prior art, the existing ground point filtering algorithm can only filter point cloud points through a fixed threshold, such as Figure 1 By taking the space range within the dotted line as a preset threshold area to screen point cloud points, through Figure 1 it is known that this area not only contains pedestrians and vehicles but also contains the greening scenes on both sides of the road. Under such conditions, the existing filtering method will inevitably misjudge the ground point cloud points, and the overall filtering effect of the ground point cloud points is not good.

[0054] The present application proposes a method for determining ground point clouds based on lidar, including:

[0055] S201, dividing all the point cloud points to be determined into multiple columnar grids according to their coordinate positions. The point cloud points to be determined are the point cloud points collected by the lidar within the ground point detection range, and all the columnar grids together form the ground point detection range;

[0056] S202, determining the retained columnar grids according to the height values of each point cloud point in each columnar grid, and determining all the point cloud points to be determined in the retained columnar grids as ground point clouds.

[0057] Exemplarily, all the point cloud points within a preset space range are divided into different columnar grids according to their coordinate positions, such as Figure 3 shown, dividing the entire space into the same columnar grids as shown in F1. According to the coordinates of the point cloud points in the space, they are divided into the corresponding columnar grids, and all the grids the same as F1 together form the ground point detection range. The F1 grid contains multiple point cloud points. Whether to retain the F1 grid is determined according to the height values of the point cloud points. The point cloud points to be determined within the F1 grid are the ground point clouds.

[0058] In this application, by dividing the point cloud points within a spatial range into corresponding grids, and then determining whether to retain the grid according to the height of the point cloud points, the point cloud points are further filtered by this method, preventing the omission of incompletely filtered point cloud points, effectively reducing the false detection rate of ground point filtering, and improving the filtering effect of non-target points.

[0059] In an implementable embodiment of this application, the method for determining ground point cloud points based on lidar further includes:

[0060] Screen out the point cloud point spatial dataset within the ground point detection range from the point cloud data collected by the lidar.

[0061] Exemplarily, assume there is a set of three-dimensional point cloud data collected by a lidar, and each point includes three coordinate values (x, y, z), representing the position of the point in three-dimensional space. By defining a height threshold, the point cloud points within the height detection range can be determined. Assume the preset height threshold is between 0 and 1 meter, then all the point cloud data between 0 and 1 meter constitutes the point cloud spatial dataset. Specifically, there is a point cloud point with coordinates (3, 4, 0.5). Determine whether this point is within the ground point detection range. The x coordinate of this point is 3, the y coordinate is 4, and the z coordinate is 0.5. In this case, the z coordinate of this point is within the range of 0 to 1 meter, so this point can be used as a point that meets the ground point detection range.

[0062] This application traverses all the point cloud data through this method, screens out the qualified point cloud point spatial dataset, preprocesses the point cloud points, reduces redundant data, and provides convenience for further filtering of point cloud points.

[0063] In an implementable embodiment of this application, determining the ground point cloud points within each column grid according to the height value of each point cloud point within each column grid includes:

[0064] Determine whether each column grid is retained according to the height value of the lowest ground point corresponding to each column grid and the height values of all the point cloud points within each column grid.

[0065] Exemplarily, assume there is a column grid containing point cloud data, and each point cloud data has corresponding three-dimensional coordinates (x, y, z). Assume the height of the lowest ground point is 0.2 meters. Preset a height range (0.5, 0.8), and determine whether to save the column grid by judging whether the difference between the point cloud points in the column grid and the lowest ground point is within the preset height range. Specifically, if the point cloud coordinates in the column grid at this time are: D1(6, 5, 0.6), D2(4, 6, 0.9), D3(5, 6, 0.4), D4(3, 4, 0.5), and the z coordinates of D1, D2, D3, and D4 are 0.6, 0.9, 0.4, and 0.5 respectively, subtract the height of the lowest ground point from the vertical coordinates of these point cloud points, and calculate the differences as: 0.4, 0.7, 0.2, 0.3. Since the preset height range is (0.5, 0.8), D2 is the point cloud point that meets the conditions, so this column grid can be retained.

[0066] In this application, it is determined whether to retain the column grid by the difference between the height values of all point cloud points in each column grid and the height value of the lowest ground point. Through this step, the filtering effect of the point cloud points is improved.

[0067] In an implementable embodiment of this application, the step of determining the ground point cloud points in each column grid according to the height value of each point cloud point in each column grid further includes:

[0068] S401, sort all the point cloud points in each column grid according to the height value, and determine the to-be-determined point cloud point with the lowest height value;

[0069] S402, determine whether to retain each column grid according to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each column grid and the height values of all the point cloud points in each column grid.

[0070] Exemplarily, assume there is a cylindrical grid containing point cloud data, and each point cloud data has corresponding three-dimensional coordinates (x, y, z). First, traverse all the point cloud points within the cylindrical grid, and determine the lowest ground point height based on the z-axis coordinate values of all the point cloud points. Specifically, assume that all the point cloud points within the cylindrical grid are B1(1, 3, 1), B2(1, 2, 0.5), B3(2, 1, 0.8), B4(3, 1, 0.4), B5(2, 2, 0.3), B6(3, 1, 0.1), B7(4, 3, 0.6). At this time, by comparing the z-axis coordinates of all the point cloud points, it is found that the z-axis coordinate of B6 is the lowest ground point height of 0.1 meters. Preset a height range (0, 1), and determine whether to save the cylindrical grid by judging whether the difference between the point cloud points within the cylindrical grid and the lowest ground point is within the preset height range. Since the z-coordinates of B1, B2, B3, B4, B5, B6, B7 are 1, 0.5, 0.8, 0.4, 0.3, 0.1, 0.6 respectively, subtract the z-axis coordinates of these point cloud points from the lowest ground point height, and calculate the differences as 0.9, 0.4, 0.7, 0.3, 0.2, 0, 0.5 respectively. The preset height range is (0, 1), so all the point cloud points are qualified point cloud points, and therefore this cylindrical grid can be retained.

[0071] This application first traverses to find the lowest ground point, and then determines whether to retain the cylindrical grid by the difference between the height values of all the point cloud points within each cylindrical grid and the lowest ground point height value. Through this step, the filtering effect of the ground point cloud points is improved.

[0072] In an implementable embodiment of this application, determining the ground point cloud points within each cylindrical grid according to the height values of each point cloud point within each cylindrical grid further includes:

[0073] S501, for each cylindrical grid, repeatedly compare the height values of any two point cloud points, and compare the point cloud point with the lower height value with any other point cloud point until all point cloud points are compared. The point cloud point with the lower height value obtained from the last comparison is used as the point cloud point to be determined with the lowest height value;

[0074] S502, determine whether to retain each cylindrical grid according to the height value of the point cloud point to be determined with the lowest height value corresponding to each cylindrical grid and the height values of all the point cloud points within each cylindrical grid.

[0075] Exemplarily, assume there is a cylindrical grid containing point cloud data, and each point cloud data has corresponding three-dimensional coordinates (x, y, z). First, compare the height values of any two point cloud points, and compare the point cloud point with a lower height value with any other point cloud point until each point cloud point has been compared. The point cloud point with the lower height value obtained from the last comparison is used as the point cloud point with the lowest height to be determined. Specifically, assume all the point cloud points within the cylindrical grid are A1(2, 3, 0.5), A2(1, 3, 0.5), A3(2, 4, 0.8), A4(1, 1, 0.4), A5(4, 2, 0.7), A6(3, 2, 0.1), A7(2, 1, 0.6). First, compare the z-axis coordinates of A1 and A2. The z-axis coordinates of A1 and A2 are both 0.5. Therefore, 0.5 is used as the height of the lowest point cloud point to be determined. Continue to compare the z-axis coordinate of point A1 with the z-axis coordinate of point A3. 0.8 is greater than 0.5, and 0.5 is used as the height of the lowest point cloud point to be determined. Continue to compare the z-axis coordinate of point A1 with the z-axis coordinate of point A4. 0.4 is less than 0.5. At this time, 0.4 is used as the height of the lowest point cloud point to be determined. Continue to compare the z-axis coordinate of point A4 with the z-axis coordinate of point A5. 0.4 is less than 0.7, and 0.4 is used as the height of the lowest point cloud point to be determined. Continue to compare the z-axis coordinate of point A4 with the z-axis coordinate of point A6. 0.1 is less than 0.4, and 0.1 is used as the height of the lowest point cloud point to be determined. Continue to compare the z-axis coordinate of point A6 with the z-axis coordinate of point A7. 0.1 is less than 0.6, and finally 0.1 is used as the height of the lowest point cloud point. Preset a height range (0, 1), and determine whether to save the cylindrical grid by judging whether the difference between the point cloud points within the cylindrical grid and the lowest ground point is within the preset height range. Since the z coordinates of A1, A2, A3, A4, A5, A6, A7 are: 0.5, 0.5, 0.8, 0.4, 0.7, 0.1, 0.6, calculate the differences between the z-axis coordinates of these point cloud points and the height of the lowest ground point, and the differences are respectively: 0.4, 0.4, 0.7, 0.3, 0.6, 0, 0.5. The preset height range is (0, 1). Therefore, all point cloud points are qualified point cloud points, and thus this cylindrical grid can be retained.

[0076] This step determines the height of the lowest ground point within the grid through real-time comparison, and then determines whether to retain the cylindrical grid based on the differences between the height values of all point cloud points within each cylindrical grid and the height value of the lowest ground point. Through this step, the filtering effect of the ground point cloud points is further improved.

[0077] In an implementable embodiment of the present application, determining whether to retain each cylindrical grid according to the height value of the point cloud point with the lowest height corresponding to each cylindrical grid and the height values of all point cloud points within each cylindrical grid includes:

[0078] S601. For each column grid, according to the difference between the height value of the to-be-determined point cloud point with the lowest corresponding height value and the height values of each to-be-determined point cloud point within the column grid, and in combination with a preset target height distribution range, determine the number of qualified point cloud points in each column grid;

[0079] S602. According to the number of qualified point cloud points in each column grid, and in combination with a preset filtering threshold parameter, determine whether each column grid is to be retained.

[0080] Exemplarily, assume that in column grid F1, a preset target height distribution range is (0, 0.5), and the preset filtering threshold parameter is 2. Now, there are point cloud points C1(5, 5, 0.8), C2(6, 4, 0.5), C3(2, 5, 0.2), C4(3, 4, 0.6), C5(1, 2, 0.3), C6(4, 4, 0.7), C7(4, 3, 0.9) within grid F1. The height of the lowest to-be-determined point cloud point determined by the above method is 0.2. At this time, the height difference between C1 and the lowest to-be-determined point cloud point is 0.6, which does not conform to the target height distribution range; the height difference between C2 and the lowest to-be-determined point cloud point is 0.3, which conforms to the target height distribution range; the height difference between C3 and the lowest to-be-determined point cloud point is 0, which conforms to the target height distribution range; the height difference between C4 and the lowest to-be-determined point cloud point is 0.4, which conforms to the target height distribution range; the height difference between C5 and the lowest to-be-determined point cloud point is 0.1, which conforms to the target height distribution range; the height difference between C6 and the lowest to-be-determined point cloud point is 0.5, which conforms to the target height distribution range; the height difference between C7 and the lowest to-be-determined point cloud point is 0.7, which does not conform to the target height distribution range. In this embodiment, C2 - C6 all conform to the target height distribution range, and the number of point cloud points conforming to the target height distribution range exceeds the preset filtering threshold parameter 2. Therefore, this column grid F1 is determined to be retained.

[0081] This application further refines the filtering of point cloud points through this step, effectively reducing the false detection rate of ground point filtering and improving the filtering effect of non-target points.

[0082] In an implementable embodiment of this application, the step of determining the number of qualified point cloud points in each column grid according to the difference between the height value of the to-be-determined point cloud point with the lowest corresponding height value and the height values of each to-be-determined point cloud point within the column grid, and in combination with a preset target height distribution range, includes:

[0083] S701. For each to-be-determined point cloud point, judge whether the difference is within the target height distribution range;

[0084] S702. If so, regard the corresponding to-be-determined point cloud point as a qualified point cloud point;

[0085] S703 calculates the number of qualified point cloud points in each column grid.

[0086] Exemplarily, if in grid F2, the target height distribution range is set to (0.1, 0.2), and there are three to-be-determined point cloud points D1(1, 1, 0.2), D2(2, 2, 0.1), D3(3, 2, 0.5) in grid F2. At this time, the lowest height value of the to-be-determined point cloud points is 0.1. Subtract the height value of each to-be-determined point cloud point from the lowest to-be-determined height value to determine the difference. At this time, the difference of D1 is 0.1, which conforms to the target height distribution range, and D1 is used as a qualified point cloud point. The difference of D2 is 0, which does not conform to the target height distribution range, so D2 is an unqualified point cloud point. The difference of D3 is 0.4, which does not conform to the target height distribution range, so D3 is an unqualified point cloud point. In summary, in grid F2, only D1 conforms to the target height distribution range, so the number of qualified point cloud points is 1.

[0087] This application effectively reduces the false detection rate of ground point filtering and improves the filtering effect of non-target points through this method.

[0088] In an implementable embodiment of this application, determining whether to retain each column grid according to the number of qualified point cloud points in each column grid and combining a preset filtering threshold parameter includes:

[0089] Judge whether the number of qualified point cloud points corresponding to each column grid is higher than the filtering threshold parameter. If it is higher, determine to retain the column grid.

[0090] Exemplarily, the preset filtering value parameter is 10. Suppose the number of qualified point cloud points in column grid A is 15, and the number of qualified point cloud points in column grid B is 8. According to the set filtering threshold parameter of 10 points, determine to retain column grid A because the number of its qualified point cloud points is higher than 10, and exclude column grid B because the number of its qualified point cloud points is lower than or equal to 10.

[0091] This application determines whether to retain each column grid according to the number of qualified point cloud points in each column grid and the preset filtering threshold parameter, so as to realize the screening and processing of point cloud data.

[0092] Figure 8 It is a schematic flow diagram of a complete embodiment of a method for determining ground point cloud points based on lidar in this application. Exemplarily, for a road scene, see Figure 9, first collect all the point cloud data of the entire scene. The dashed box shown by z1 is the preset range of point cloud data, and grid division is performed on the part within this spatial range. z2 is the effective detection range on the z-axis. Then set all the required range parameters. Set the point cloud data range as: [Xmin, Ymin, Zmin, Xmax, Ymax, Zmax]. Points exceeding the point cloud data range will be excluded first. Since the point cloud has the characteristics of being dense at close range and sparse at long range, set the required point cloud range: [Xfilater, Yfilater]. The setting of this parameter only filters the ground points at close range. According to the coordinates of all points in the point cloud data, divide the three-dimensional point cloud data space into m*n*2 cylindrical grids, and the size of each grid is equal. In addition, set the target distribution range on the z-axis as [Emin, Emax] and the filtering threshold parameter T = 1. Specifically, traverse all the point cloud points in z1, and count the number of points that meet the E min <Z p -Z grid ≤E max condition. Compare the number threshold T of the number of points that meet the E min <Z p -Z grid ≤E max condition in each cylindrical grid. When the number of points that meet the condition is greater than T, it is considered that there is a target in this grid, and all the points in this grid are retained. Put the points of all the retained grids together, and the filtering of the ground point cloud points is completed.

[0093] This application effectively reduces the false detection rate of ground point filtering and improves the filtering effect of non-target points through this method.

[0094] The above mainly introduced the method of the embodiments of the present application in combination with the accompanying drawings. It should be noted that all the numerical values mentioned above are only for illustration and do not constitute specific limitations to the present application. At the same time, it should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence, these steps are not necessarily executed in the order shown in the figures. Unless there is a clear description in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps. Next, the device of the embodiments of the present application will be introduced in combination with the accompanying drawings. For the sake of brevity, when introducing the device below, appropriate omissions will be made, and the relevant content can be referred to the relevant descriptions in the above method introduction and will not be repeated.

[0095] Figure 10 FIG. 4 is a schematic structural diagram of a ground point cloud point determination device 900 provided by an embodiment of the present application. For the convenience of description, only the parts related to the embodiments of the present application are shown.

[0096] A ground point cloud point determination device based on lidar includes:

[0097] A preprocessing unit 1001, configured to divide all the to-be-determined point cloud points into a plurality of columnar grids according to the coordinate positions, where the to-be-determined point cloud points are the point cloud points collected by the lidar within the ground point detection range, and all the columnar grids together form the ground point detection range;

[0098] A processing unit 1002, configured to determine the retained columnar grids according to the height values of each point cloud point in each columnar grid, and determine all the to-be-determined point cloud points in the retained columnar grids as ground point cloud points.

[0099] Figure 11 FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 11 shown, the electronic device 1100 in this embodiment includes: at least one processor 1101 ( Figure 11 only one is shown), a memory 1102, and a computer program 1103 stored in the memory 1102 and executable on at least one processor 1101. When the processor 1101 executes the computer program 1103, the steps in the above embodiments are implemented.

[0100] The processor 1101 can be a Central Processing Unit (CPU), and the processor 1101 can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0101] In some embodiments, the memory 1102 can be an internal storage unit of the electronic device 1100, such as the hard disk or memory of the electronic device 1100. In other embodiments, the memory 1102 can also be an external storage device of the electronic device 1100, such as a plug-in hard disk equipped on the electronic device 1100, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 1102 can also include both the internal storage unit and the external storage device of the electronic device 1100. The memory 1102 is used to store an operating system, application programs, Boot Loader data, and other programs, such as the program code of a computer program. The memory 1102 can also be used to temporarily store data that has been output or will be output.

[0102] It should be noted that, regarding the information interaction, execution process, etc. between the above units, since they are based on the same concept as the method embodiments of the present application, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not elaborated herein.

[0103] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit is used as an example. In practical applications, the above functions can be allocated to different functional units or modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0104] The embodiment of this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

[0105] The embodiment of this application provides a computer program product. When the computer program product runs on a computer, it can implement the above various methods.

[0106] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0107] It should be understood that the sequence numbers of the steps in the above embodiments do not indicate the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. In the description, specific details such as a specific system structure and technology are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application.

[0108] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0109] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0111] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for determining ground point cloud points based on lidar, characterized in that, Including: Dividing all the to-be-determined point cloud points into multiple cylindrical grids according to their coordinate positions, where the to-be-determined point cloud points are the point cloud points collected by the lidar within the ground point detection range, and all the cylindrical grids combine to form the ground point detection range; Determining the retained cylindrical grids according to the height values of each point cloud point within each cylindrical grid, and determining all the to-be-determined point cloud points within the retained cylindrical grids as ground point cloud points.

2. The method for determining ground point cloud points based on lidar according to claim 1, characterized in that, The method further includes: Filtering out the point cloud point spatial dataset within the ground point detection range from the point cloud data collected by the lidar.

3. The method for determining ground point cloud points based on lidar according to claim 1, wherein The determining of the ground point cloud points within each cylindrical grid according to the height values of each point cloud point within each cylindrical grid includes: Determining whether each cylindrical grid is retained according to the height value of the lowest ground point corresponding to each cylindrical grid and the height values of all the point cloud points within each cylindrical grid.

4. The method for determining ground point cloud points based on lidar according to claim 1, characterized in that, The determining of the ground point cloud points within each cylindrical grid according to the height values of each point cloud point within each cylindrical grid further includes: Sorting all the point cloud points within each cylindrical grid according to their height values, and determining the to-be-determined point cloud point with the lowest height value; Determining whether each cylindrical grid is retained according to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each cylindrical grid and the height values of all the point cloud points within each cylindrical grid.

5. A method for determining ground point cloud points based on lidar according to claim 1, characterized in that, The determining of the ground point cloud points within each cylindrical grid according to the height values of each point cloud point within each cylindrical grid further includes: For each cylindrical grid, repeatedly comparing the height values of any two point cloud points, and comparing the point cloud point with the lower height value with any other point cloud point until all the point cloud points are compared, and taking the point cloud point with the lower height value obtained in the last comparison as the to-be-determined point cloud point with the lowest height value; Determining whether each cylindrical grid is retained according to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each cylindrical grid and the height values of all the point cloud points within each cylindrical grid.

6. Any of the methods for determining ground point clouds based on lidar according to claims 1-5, characterized in that, The determining of whether each cylindrical grid is retained according to the height value of the to-be-determined point cloud point with the lowest height value corresponding to each cylindrical grid and the height values of all the point cloud points within each cylindrical grid includes: For each cylindrical grid, according to the difference between the height value of the to-be-determined point cloud point with the lowest height value and the height value of each to-be-determined point cloud point within the cylindrical grid, and in combination with the preset target height distribution range, determining the number of qualified point cloud points in each cylindrical grid; Determining whether each cylindrical grid is retained according to the number of qualified point cloud points in each cylindrical grid and in combination with the preset filtering threshold parameter.

7. The method for determining ground point cloud points based on lidar according to claim 6, characterized in that, The determining of the number of qualified point cloud points in each cylindrical grid according to the difference between the height value of the to-be-determined point cloud point with the lowest height value and the height value of each to-be-determined point cloud point within the cylindrical grid, and in combination with the preset target height distribution range includes: For each to-be-determined point cloud point, determining whether the difference is within the target height distribution range; If so, taking the corresponding to-be-determined point cloud point as a qualified point cloud point; Calculating the number of qualified point cloud points in each cylindrical grid.

8. The method for determining ground point cloud points based on lidar according to claim 6, wherein, Determining whether to retain each column grid according to the number of qualified point cloud points in each column grid and in combination with a preset filtering threshold parameter includes: Judging whether the number of qualified point cloud points corresponding to each column grid is higher than the filtering threshold parameter, and if so, determining to retain the column grid.

9. A ground point cloud point determination device based on lidar, characterized in that, Including: A preprocessing unit configured to divide all to-be-determined point cloud points into a plurality of column grids according to coordinate positions, where the to-be-determined point cloud points are point cloud points collected by a lidar within a ground point detection range, and all column grids form the ground point detection range in combination; A processing unit configured to determine the retained column grids according to the height values of each point cloud point in each column grid, and determine all to-be-determined point cloud points in the retained column grids as ground point cloud points.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by an electronic device, it implements the method according to any one of claims 1 to 8.

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