A method, apparatus and system for eliminating duplicate points in point cloud data

Abstract

The application discloses a method, device and system for eliminating repeated points in point cloud data, the method comprising: obtaining a second track point in second track data within a preset spatial range around a first track point in first track data; comparing a difference between the heading angles of the first track point and the second track point with a preset angle threshold value, and if the difference is less than the angle threshold value, selecting the first track point or the second track point as a to-be-deleted track point; and deleting a laser point matching the to-be-deleted track point from point cloud data corresponding to the track data to which the to-be-deleted track point belongs. The method uses the repeatability of track points to find repeated laser points, thereby improving the accuracy of finding repeated laser points, improving the efficiency of querying repeated laser points in point cloud data, reducing the data volume of point cloud data, and improving the production efficiency of high-precision maps.

Description

Technical Field

[0001] This invention relates to the field of high-precision map production, and in particular to a method, apparatus and system for eliminating duplicate points in point cloud data. Background Technology

[0002] Currently, high-precision maps require cartographers to use data creation platforms for production or quality checks. During the high-precision map production process, a high-precision data acquisition vehicle is needed to collect laser point clouds for map creation. These laser point clouds are a collection of massive laser points obtained based on the principles of laser measurement. When a laser beam strikes an object's surface, reflection occurs, and a vast number of laser points can be collected through this reflection, thus forming a laser point cloud.

[0003] For the same road segment, a high-precision data acquisition vehicle will conduct data collection operations on that road multiple times. However, the point cloud data obtained from different collection operations will vary due to differences in environmental factors and the operating status of the acquisition equipment, resulting in duplicate point cloud data. Because of the high-precision... Figure 1 High-precision map production typically requires point cloud data obtained from multiple acquisition operations. Therefore, eliminating duplicate points in point cloud data obtained from different acquisition operations is a problem that must be solved. Summary of the Invention

[0004] In view of the technical defects and drawbacks existing in the prior art, embodiments of the present invention provide a method, apparatus and system for eliminating duplicate points in point cloud data to overcome the above problems or at least partially solve the above problems.

[0005] As one aspect of this invention, a method for eliminating duplicate points in point cloud data is provided, which may include:

[0006] Obtain the second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data;

[0007] The difference between the heading angles of the first trajectory point and the second trajectory point is compared with a preset angle threshold. If the difference is less than the angle threshold, the first trajectory point or the second trajectory point is selected as the trajectory point to be deleted.

[0008] Delete the laser point that matches the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

[0009] As a second aspect of the present invention, an apparatus for eliminating duplicate points in point cloud data is provided, which may include:

[0010] The acquisition module is used to acquire a second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data;

[0011] The comparison module is used to compare the difference in heading angle between the first trajectory point and the second trajectory point with a preset angle threshold. If the difference is less than the angle threshold, the first trajectory point or the second trajectory point is selected as the trajectory point to be deleted.

[0012] The deletion module is used to delete laser points that match the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

[0013] Optionally, it may also include: a matching module, used to match the point cloud data collected during the acquisition of trajectory data with the trajectory data to obtain the matching laser point for each trajectory point included in the trajectory data.

[0014] As a third aspect of the present invention, a system for eliminating duplicate points in point cloud data is provided, which may include:

[0015] The data acquisition vehicle is used to collect point cloud data while collecting trajectory data;

[0016] The server is equipped with the device for eliminating duplicate points in point cloud data as described in the second aspect above, used to eliminate duplicate points in the trajectory data and point cloud data collected by the acquisition vehicle.

[0017] As a fourth aspect of the present invention, therein is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for eliminating duplicate points in point cloud data as described in the first aspect.

[0018] As a fifth aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for eliminating duplicate points in point cloud data as described in the first aspect above.

[0019] The embodiments of the present invention achieve at least the following technical effects:

[0020] Based on the preset spatial distance and heading angle difference of trajectory points in multiple collected trajectory data, trajectory points to be deleted are selected. The corresponding point cloud data is then determined according to the trajectory data to which the trajectory points to be deleted belong. Laser points matching the trajectory points to be deleted are then removed from the corresponding point cloud data. This method utilizes the repetitiveness of trajectory points to find duplicate laser points, improving both the accuracy of finding duplicate laser points and the efficiency of retrieving duplicate laser points from the point cloud data. This reduces the amount of point cloud data and improves the efficiency of high-precision map production.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures described in the written description, claims, and drawings.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 A flowchart illustrating a method for eliminating duplicate points in point cloud data, provided by an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of a data structure in a point cloud acquisition project provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram illustrating the structure for querying multiple trajectory data provided in this embodiment of the invention;

[0027] Figure 4 This is a schematic diagram of a heading angle measurement method provided in an embodiment of the present invention;

[0028] Figure 5 This is a schematic diagram of a device for eliminating duplicate points in point cloud data according to an embodiment of the present invention;

[0029] Figure 6 A flowchart illustrating another method for eliminating duplicate points in point cloud data provided in an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of another device for eliminating duplicate points in point cloud data provided in an embodiment of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of a system for eliminating duplicate points in point cloud data, provided by an embodiment of the present invention. Detailed Implementation

[0032] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0034] Example 1

[0035] This invention provides a method for eliminating duplicate points in point cloud data, such as... Figure 1 As shown, the method may include the following steps:

[0036] Step S11: Obtain the second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data.

[0037] For the same road segment, the high-precision data acquisition vehicle will conduct data acquisition operations on that road more than once. Each data acquisition operation constitutes a project. The point cloud data collected by different projects for the same road will inevitably have duplicate parts. Directly eliminating duplicate points in the point cloud data is computationally intensive and difficult to operate. However, the inventors of this application have discovered that:

[0038] 1. When the high-precision data acquisition vehicle travels on the same road on different occasions, if the direction of travel (direction of the vehicle's front) and position of the acquisition vehicle are similar, the probability of collecting duplicate point cloud data is extremely high, because in this case, the road environment scanned by the lidar equipped on the high-precision data acquisition vehicle is almost the same.

[0039] 2. When the high-precision data acquisition vehicle is driving on the road, the equipment on it will collect the vehicle's trajectory (the trajectory includes a series of positioning positions). At the same time, the lidar will scan the road environment to obtain the lidar point cloud. The lidar point cloud and the trajectory (i.e. the vehicle's positioning position) have a corresponding relationship. Therefore, for point cloud data collected from different projects, the duplicate trajectory points can be found and the duplicate points can be eliminated, which is not only efficient but also easy to implement.

[0040] Step S12: Compare the difference in heading angle between the first trajectory point and the second trajectory point with a preset angle threshold.

[0041] In this embodiment of the invention, if a second trajectory point corresponding to a first trajectory point in a first trajectory is found within a preset spatial range, it indicates that the positions of the two trajectory points are substantially close. Therefore, this application solves the problem of finding trajectory points with similar positions by using a preset range, and then determines whether the trajectory points are repeated by using the heading angle of the trajectory points. This embodiment of the invention determines whether the trajectory points are repeated by comprehensively considering the distance and direction of movement of the trajectory points.

[0042] Step S13: Select either the first trajectory point or the second trajectory point as the trajectory point to be deleted.

[0043] If the difference in heading angle between the first trajectory point and the second trajectory point in step S12 is less than a preset angle threshold, it is considered a duplicate trajectory point. Any duplicate trajectory point can be selected as the trajectory point to be deleted, or the selection can be based on the priority of the trajectory to which the trajectory point belongs.

[0044] Step S14: Delete the laser points that match the trajectory points to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory points to be deleted belong.

[0045] After finding the trajectory point to be deleted, delete the laser points that match the trajectory point to be deleted, thereby eliminating duplicate points in the point cloud data.

[0046] This invention selects trajectory points to be deleted based on the distance and heading angle differences of trajectory points in multiple collected trajectory data. It then determines the corresponding point cloud data based on the trajectory data to which the trajectory points to be deleted belong, and deletes the laser points in the corresponding point cloud data that match the trajectory points to be deleted. This invention utilizes the repetitiveness of trajectory points to find duplicate laser points, improving both the accuracy of finding duplicate laser points and the efficiency of retrieving duplicate laser points from point cloud data. This reduces the amount of point cloud data, lowers the cost of point cloud data management, and improves the efficiency of high-precision map production.

[0047] The following is a detailed description of each step in the embodiments of the present invention:

[0048] In step S11 above, a second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data is obtained.

[0049] The trajectory data is collected during a point cloud acquisition project. In one such project, a high-precision acquisition vehicle is used to collect point cloud data (laser point cloud) for creating a high-precision map. This project includes trajectory data generated during the vehicle's movement and point cloud data of the road environment collected using lidar. Because the acquisition range of a single point cloud acquisition project is limited, it is necessary to combine point cloud data collected from multiple projects to generate a large-scale, integrated high-precision map.

[0050] For example, refer to Figure 2 As shown, this is a schematic diagram of the data structure in a point cloud acquisition project. The black dots represent the trajectory data of the high-precision acquisition vehicle. The trajectory data consists of multiple trajectory points, and the range of the lines on both sides of the black dots represents the range of the point cloud data.

[0051] Each trajectory point in the above trajectory data may include the timestamp information, coordinate information, direction of motion information, and velocity information of the trajectory point; the above point cloud data may include the timestamp information, coordinate information, and laser reflection intensity information of the point cloud.

[0052] For example, refer to Figure 3 As shown in the figure, there are three point cloud acquisition projects: point cloud acquisition project A, point cloud acquisition project B, and point cloud acquisition project C. These correspond to three trajectory data sets: trajectory data A, trajectory data B, and trajectory data C. Each trajectory data set shows the movement direction of the high-precision acquisition vehicle, and each trajectory data set contains four trajectory points: trajectory points a0, a1, a2, and a3; trajectory points b0, b1, b2, and b3; and trajectory points c0, c1, c2, and c3. This embodiment of the invention uses... Figure 3 Trajectory data A in the first trajectory data is used to search within a preset spatial range around the first trajectory point in the first trajectory data. For example, the search is performed within a preset spatial range around the first trajectory point a0. The preset spatial range refers to... Figure 3 As shown, this area can be within 1m on either side of the trajectory point's direction of travel, or it can be a spherical region with trajectory point a0 as its center and a radius of 1m. This embodiment of the invention does not specifically limit this. (Refer to...) Figure 3 As shown, trajectory point b0 is within a preset spatial range surrounding the first trajectory point a0, but trajectory point c0 and other trajectory points are not within the preset spatial range surrounding the first trajectory point a0. Figure 3 The trajectory point b0 in the first trajectory point a0 is used as the second trajectory point. Of course, if at least one trajectory point is found within a preset range around the first trajectory point a0, it can also be used as the second trajectory point. In this embodiment of the invention, the number of second trajectory points is not specifically limited; it can be 0 trajectory points, 1 trajectory point, or multiple trajectory points.

[0053] Of course, the aforementioned surrounding preset space range in this step also includes height differences; the comparison is not only performed on a plane but also within a spatial region. In this embodiment of the invention, the height of the trajectory point can be obtained through the coordinate information of the trajectory point, and the obtained height difference is compared with a preset height threshold. The main purpose of this step is to avoid treating trajectory points with different height differences as duplicate trajectory points and to avoid deleting the point cloud data corresponding to non-duplicate trajectory points.

[0054] For example, when a high-precision data acquisition vehicle collects high-precision map information of an elevated bridge, the trajectory data of the high-precision data acquisition vehicle can be one or more. When the trajectory data of the elevated bridge is collected in multiple ways, it is necessary to determine whether there are duplicate trajectory data. The specific determination method is described in the steps above. Then, the height difference between the first trajectory point and the second trajectory point is compared with the preset height threshold. For example, if the preset height threshold is 3m, then the height difference between the first trajectory point and the second trajectory point is compared with the preset height threshold of 3m.

[0055] If the second trajectory point can be obtained in step S11 above, then step S12 is executed, that is, the difference in heading angle between the first trajectory point and the second trajectory point is compared with a preset angle threshold.

[0056] The heading angle refers to the angle between the vehicle's direction of travel and the Earth's North Pole. Specifically, in this embodiment of the invention, each trajectory point has a heading angle, which is the angle between the line connecting the current trajectory point and the next trajectory point and due north.

[0057] This step also includes: for each trajectory, obtaining the heading angle of each trajectory point included in the trajectory. For example, referring to... Figure 4 As shown, in a trajectory dataset, three trajectory points are M, M0, and M1. The angle between the line connecting trajectory point M and trajectory point M0 and the due north direction is θ, where θ is the heading angle of trajectory point M. Similarly, the angle between the line connecting trajectory point M0 and trajectory point M1 and the due north direction is α, where α is the heading angle of trajectory point M0. Of course, in a trajectory dataset, the heading angle of the last trajectory point can be the same as that of the second-to-last trajectory point; this embodiment of the invention does not impose a specific limitation on this.

[0058] For example, if the heading angles of the trajectory points in trajectory data A and trajectory data B in step S11 are θ1, α1... and θ2, α2... respectively, then the heading angle difference between the first trajectory point a0 and the second trajectory point b0 in step S12 is |θ1-θ2|. The absolute value between them is taken as the difference and compared with the preset angle threshold.

[0059] If the difference in heading angle between the first trajectory point and the second trajectory point in step S12 is less than a preset angle threshold, then step S13 is executed, i.e., either the first trajectory point or the second trajectory point is selected as the trajectory point to be deleted. If the positions and directions of movement of the two trajectory points are consistent, they can be considered to coincide, and the corresponding high-precision data acquisition vehicle's acquisition position and direction of movement are consistent. For example, in this embodiment of the invention, the preset angle threshold is 0.1°. If the difference in heading angle between the first trajectory point and the second trajectory point is less than 0.1°, then step S13 is executed.

[0060] Specifically, in this step, the trajectory points in the trajectory data with lower priority can be selected as the trajectory points to be deleted based on the priority of the first trajectory data and the second trajectory data.

[0061] The priority refers to a convention, which is a parameter used by a computer time-sharing operating system to determine the priority level of each job program in receiving system resources when processing multiple job programs. In this embodiment of the invention, the point cloud data corresponding to the trajectory data with higher priority is retained, while the point cloud data corresponding to the trajectory data with lower priority is deleted.

[0062] Optionally, the priority is determined based on at least one of the following: acquisition time, acquisition accuracy, and acquisition environment. For example, the more recent the acquisition time, the higher the priority; the higher the acquisition accuracy, the higher the priority; and in the acquisition environment, favorable weather conditions have a higher priority than unfavorable weather conditions.

[0063] In step S14 above, laser points that match the trajectory points to be deleted are deleted from the point cloud data corresponding to the trajectory data to which the trajectory points to be deleted belong.

[0064] By comparing the heading angle difference between the second trajectory point within a preset spatial range around the first trajectory point and a preset angle threshold in step S12, it can be determined whether the second trajectory point is a trajectory point that overlaps with the first trajectory point. The basis for this determination is that if two trajectory points on different point cloud acquisition projects are close enough and move in the same direction, the two trajectory points can be set as overlapping trajectory points. Therefore, the point cloud data collected at these two trajectory points are also overlapping. Only one set of point cloud data needs to be retained to complete the production of a high-precision map. The overlapping point cloud data needs to be deleted.

[0065] For example, since the first trajectory point and the second trajectory point are duplicate trajectory points, the laser point corresponding to either trajectory point can be deleted. Alternatively, the laser point corresponding to the trajectory point to be deleted can be determined according to the priority of the trajectory to which the trajectory point belongs. This embodiment of the invention does not impose specific limitations on this.

[0066] In one specific embodiment, before deleting the laser point that matches the trajectory point to be deleted, the method may further include: matching the point cloud data collected when collecting trajectory data with the trajectory data to obtain the laser point that matches each trajectory point included in the trajectory data.

[0067] For example, refer to Figure 2As shown, a point cloud acquisition project includes point cloud data and trajectory data. Point cloud data is a collection of massive data of multiple laser points. One trajectory point can correspond to a large number of laser points in the point cloud data. In this step, the point cloud data is matched with the trajectory points in the trajectory data to determine the correspondence between the point cloud data and the trajectory points. This is to facilitate the deletion of laser points in the point cloud data that match the trajectory point to be deleted.

[0068] In one specific embodiment, the acquisition time of each laser point in the point cloud data can be compared with the acquisition time of the trajectory points in the trajectory data, and the laser point can be matched to the trajectory point in the trajectory data with the smallest difference in acquisition time.

[0069] Because both the laser points in the point cloud data and the trajectory points in the trajectory data contain timestamp information, and each laser point in the point cloud data can correspond to a trajectory point in the same point cloud acquisition project, a method of comparing timestamps can be used to match the trajectory points in the point cloud data with those in the trajectory data. For example, refer to... Figure 3 As shown, taking the point cloud data between adjacent trajectory points a0 and a1 in trajectory data A as an example, the acquisition time of the laser points in the point cloud data acquired in point cloud acquisition project A is compared with the acquisition time of trajectory points a0 and a1, respectively. The laser point is matched to the trajectory point in the trajectory data with the smallest difference in acquisition time. Of course, in order to make the calculation method of the dead angle of the above trajectory points consistent with the matching method of trajectory points and point cloud data, the laser points whose acquisition time falls within the time period between trajectory points a0 and a1 can be used as the point cloud data corresponding to trajectory point a0. According to the above comparison method, the point cloud data matched for each trajectory point is finally obtained.

[0070] In another specific embodiment, the point cloud data can be projected onto the trajectory line formed by the trajectory data, and the laser point can be matched to the trajectory point in the trajectory data that is closest to its projection position.

[0071] Because the coordinate information of the laser points in the point cloud data and the trajectory points in the trajectory data is obtained by referring to the above, Figure 3As shown, the trajectory line between adjacent trajectory points a0 and a1 in trajectory data A is taken as the projected trajectory line. Laser points in the point cloud data acquired in point cloud acquisition project A are projected onto the aforementioned trajectory line. The laser points are matched to the trajectory points in the trajectory data closest to their projected positions. Alternatively, the laser points whose projected positions are between two trajectories a0 and a1 can be determined as the point cloud data corresponding to trajectory point a0, and vice versa. In this embodiment, to ensure that the method for calculating the dead angle of the trajectory points is consistent with the method for matching trajectory points and point cloud data, the laser points whose projected positions are between two trajectories a0 and a1 can be determined as the point cloud data corresponding to trajectory point a0. Following the above projection method, the laser points in the point cloud data matched for each trajectory point are finally obtained. Of course, before projection, the point cloud data and trajectory points need to be converted to the same coordinate system, for example, the coordinate system of the trajectory points is converted to the same projection coordinate system as the point cloud data, and then the point cloud data and trajectory points in the trajectory data are matched.

[0072] This invention selects trajectory points to be deleted based on preset spatial distances and heading angle differences of trajectory points from multiple collected trajectory data. It then determines the corresponding point cloud data based on the trajectory data to which the trajectory points to be deleted belong, and deletes the laser points in the corresponding point cloud data that match the trajectory points to be deleted. This invention utilizes the repetitiveness of trajectory points to find duplicate laser points, improving both the accuracy of finding duplicate laser points and the efficiency of retrieving duplicate laser points from point cloud data. This reduces the amount of point cloud data, lowers the cost of point cloud data management, and improves the efficiency of high-precision map production.

[0073] It should be noted that, in this embodiment of the invention, it is possible to simultaneously search within a preset range around all trajectory points in the trajectory data for points that are duplicated with trajectory points in other trajectory data, and then use the found trajectory points as second trajectory points, and then simultaneously perform the above comparison and deletion steps.

[0074] Based on the same inventive concept, embodiments of the present invention provide a device for eliminating duplicate points in point cloud data, such as... Figure 5 As shown, the device may include: an acquisition module 10, a comparison module 20, and a deletion module 30, and may also include a matching module 40. Its working principle is as follows:

[0075] The acquisition module 10 acquires a second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data. In this device, the acquisition module 10 also acquires the heading angle of each trajectory point included in each trajectory.

[0076] The comparison module 20 compares the difference in heading angle between the first trajectory point and the second trajectory point with a preset angle threshold. If the difference is less than the angle threshold, either the first trajectory point or the second trajectory point is selected as the trajectory point to be deleted. Specifically, the comparison module 20 selects the trajectory point in the trajectory data with lower priority as the trajectory point to be deleted based on the priority of the first trajectory data and the second trajectory data; wherein, the priority is determined based on at least one of the following: acquisition time, acquisition accuracy, and acquisition environment.

[0077] The deletion module 30 deletes the laser point data that matches the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

[0078] In one specific embodiment, the matching module 40 matches the point cloud data acquired during trajectory data acquisition with the trajectory data to obtain a matching laser point for each trajectory point included in the trajectory data. Specifically, the matching module 40 can compare the acquisition time of each laser point in the point cloud data with the acquisition time of the trajectory points in the trajectory data, and match the laser point to the trajectory point in the trajectory data with the smallest difference in acquisition time. Specifically, the matching module 40 can also project the point cloud data onto the trajectory line formed by the trajectory data, and match the laser point to the trajectory point in the trajectory data that is closest to its projected position.

[0079] For the technical effects and other specific descriptions of this embodiment, please refer to the relevant content of Embodiment 1 above, which will not be repeated here.

[0080] Example 2

[0081] This invention provides another method for eliminating duplicate points in point cloud data, such as... Figure 6 As shown, the method may include the following steps:

[0082] Step S11: Obtain the second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data.

[0083] Step S12: Compare the difference in heading angle between the first trajectory point and the second trajectory point with a preset angle threshold.

[0084] Step S13: Select either the first trajectory point or the second trajectory point as the trajectory point to be deleted.

[0085] Step S14: Delete the laser points that match the trajectory points to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory points to be deleted belong.

[0086] Step S15: Repeat the acquisition and deletion steps for the next trajectory point of the first trajectory point.

[0087] The explanations of terms, examples, and beneficial effects of steps S11 to S14 in this embodiment of the invention are the same as those in Embodiment 1, and will not be repeated here.

[0088] This embodiment of the invention may further include step S15. For example, if the second trajectory point is not obtained in step S11, or if the difference is greater than or equal to a preset angle threshold in step S12, it proves that the first trajectory point has no other trajectory points collected by the trajectory project that overlap with it. When step S13 is executed, or the difference in heading angle between the first and second trajectory points in step S12 is greater than or equal to the preset angle threshold, or the second trajectory point is not found in step S11, step S15 is executed until all trajectory points in the point cloud acquisition project have completed the above acquisition, search, and deletion steps.

[0089] It should be noted that, compared with Embodiment 1 above, in this embodiment, the acquisition and deletion steps are repeated for the next trajectory point of the first trajectory point in step S15 until all trajectory points in the point cloud acquisition project have undergone the acquisition and deletion steps. Embodiment 1, however, can simultaneously perform the acquisition and deletion steps for all trajectory point data within a trajectory. For example, when performing the acquisition and deletion steps for all trajectory points in trajectory data A in Embodiment 1, the point cloud data corresponding to trajectory points in other trajectory data that overlap with trajectory points in trajectory data A is deleted, or the point cloud data corresponding to trajectory points in trajectory data A is deleted.

[0090] This invention improves the efficiency of retrieving duplicate point cloud data by analyzing the trajectory points according to their heading angle and surrounding predetermined spatial range, searching for and determining whether there are duplicate trajectory points from other point cloud acquisition projects. Based on the matching results between point cloud data and trajectory data, the point cloud data corresponding to duplicate trajectory points is deleted, ultimately generating large-scale point cloud data without duplication. This unique point cloud data is then used to create high-precision maps. This invention utilizes the difference in heading angle and distance between trajectory points to determine whether there are duplicate trajectory points from other point cloud acquisition projects. It matches and binds trajectory points to point cloud data based on timestamp information or coordinates, improving the efficiency of retrieving duplicate point cloud data, reducing point cloud data management costs, and increasing the efficiency of high-precision map production.

[0091] Based on the same inventive concept, embodiments of the present invention provide another apparatus for eliminating duplicate points in point cloud data, such as... Figure 7 As shown, the device may include: an acquisition module 10, a comparison module 20, a deletion module 30, a matching module 40, and a duplicate module 50, and its working principle is as follows:

[0092] The acquisition module 10 acquires a second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data. In this device, the acquisition module 10 also acquires the heading angle of each trajectory point included in each trajectory.

[0093] The comparison module 20 compares the difference in heading angle between the first trajectory point and the second trajectory point with a preset angle threshold. If the difference is less than the angle threshold, either the first trajectory point or the second trajectory point is selected as the trajectory point to be deleted. Specifically, the comparison module 20 selects the trajectory point in the trajectory data with lower priority as the trajectory point to be deleted based on the priority of the first trajectory data and the second trajectory data; wherein, the priority is determined based on at least one of the following: acquisition time, acquisition accuracy, and acquisition environment.

[0094] The deletion module 30 deletes the laser point data that matches the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

[0095] The matching module 40 matches the point cloud data collected during trajectory data acquisition with the trajectory data to obtain a matching laser point for each trajectory point included in the trajectory data. Specifically, the matching module 40 can compare the acquisition time of each laser point in the point cloud data with the acquisition time of the trajectory points in the trajectory data, and match the laser point to the trajectory point in the trajectory data with the smallest difference in acquisition time. Specifically, the matching module 40 can also project the point cloud data onto the trajectory line formed by the trajectory data, and match the laser point to the trajectory point in the trajectory data that is closest to its projected position.

[0096] The repeat module 50 repeats the acquisition and deletion steps for the next trajectory point of the first trajectory point.

[0097] For the technical effects and other specific descriptions of this embodiment, please refer to the relevant content in the above method embodiments, which will not be repeated here.

[0098] Example 3

[0099] This invention provides a system for eliminating duplicate points in point cloud data, referring to... Figure 8 As shown, the system may include: a data collection vehicle 1 and a server 2; wherein,

[0100] The data acquisition vehicle 1 is used to acquire point cloud data while acquiring trajectory data; the server 2 is equipped with the device for eliminating duplicate points in the point cloud data as described in Embodiment 2 above, which is used to eliminate duplicate points in the trajectory data and point cloud data acquired by the data acquisition vehicle 1.

[0101] For the technical effects and other specific descriptions of this embodiment, please refer to the relevant content of Embodiment 1 above, which will not be repeated here.

[0102] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0103] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for eliminating duplicate points in point cloud data, comprising: Obtain the second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data; The first trajectory data and the second trajectory data correspond to different acquisition projects; The difference between the heading angles of the first trajectory point and the second trajectory point is compared with a preset angle threshold. If the difference is less than the angle threshold, the trajectory point in the trajectory data with lower priority is selected as the trajectory point to be deleted according to the priority of the first trajectory data and the second trajectory data. Delete the laser point that matches the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

2. The method according to claim 1, further comprising: The point cloud data collected during trajectory data acquisition is matched with the trajectory data to obtain the matching laser point for each trajectory point included in the trajectory data.

3. The method according to claim 2, wherein matching the point cloud data collected during trajectory data acquisition with the trajectory data to obtain the laser point matching each trajectory point included in the trajectory data comprises: The acquisition time of each laser point in the point cloud data is compared with the acquisition time of the trajectory points in the trajectory data, and the laser point is matched to the trajectory point in the trajectory data with the smallest difference in acquisition time.

4. The method according to claim 2, wherein matching the point cloud data collected during trajectory data acquisition to obtain the matching laser point for each trajectory point included in the trajectory data, comprises: The point cloud data is projected onto the trajectory line formed by the trajectory data, and the laser point is matched to the trajectory point in the trajectory data that is closest to its projection position.

5. The method according to claim 1, further comprising: For each trajectory, obtain the heading angle of each trajectory point included in the trajectory.

6. The method according to claim 1, wherein the priority is determined based on at least one of acquisition time, acquisition accuracy, and acquisition environment.

7. An apparatus for eliminating duplicate points in point cloud data, comprising: The acquisition module is used to acquire a second trajectory point in the second trajectory data located within a preset spatial range surrounding the first trajectory point in the first trajectory data; The first trajectory data and the second trajectory data correspond to different acquisition projects; The comparison module is used to compare the difference between the heading angles of the first trajectory point and the second trajectory point with a preset angle threshold. If the difference is less than the angle threshold, the trajectory point in the trajectory data with the lower priority is selected as the trajectory point to be deleted according to the priority of the first trajectory point and the second trajectory point. The deletion module is used to delete laser points that match the trajectory point to be deleted from the point cloud data corresponding to the trajectory data to which the trajectory point to be deleted belongs.

8. A system for eliminating duplicate points in point cloud data, comprising: The data acquisition vehicle is used to collect point cloud data while collecting trajectory data; The server is equipped with the apparatus as described in claim 7, used to eliminate duplicate points in the trajectory data and point cloud data collected by the data collection vehicle.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a method for eliminating duplicate points in point cloud data as claimed in any one of claims 1 to 6.

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