Road surface contour data construction method and device

Abstract

The invention provides a road surface contour data construction method and device, and relates to the technical field of automatic driving. The method comprises the following steps of: acquiring N first moments at each first moment; according to the pose information of the vehicle at the first moment, each piece of pre-recorded historical pose information of a pose acquisition module under a second coordinate system from a starting moment to the first moment, and an initial pose conversion relation of the first coordinate system relative to the second coordinate system at the starting moment; determining a real-time pose conversion relation of a first coordinate system relative to a second coordinate system at the first moment; at each of the N first moments, according to a real-time pose conversion relation corresponding to the first moment and the road section contour point cloud data in the first coordinate system, converting the data into road section contour point cloud data in a second coordinate system, and splicing the road section contour point cloud data in the second coordinate system corresponding to the N first moments, and constructing road surface contour point cloud data.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method and device for constructing road profile data. Background Art

[0002] With the development of science and technology, autonomous driving technology has become increasingly mature, bringing convenience to people's travel. Autonomous driving needs to collect road shape data in front of the vehicle at every moment and perform corresponding driving operations based on the road shape data in front of the vehicle.

[0003] At present, the road shape data in front of the vehicle, which is used as a basis for executing corresponding driving operations, has a small amount of data and insufficient content, resulting in low reliability in executing corresponding driving operations and even safety accidents. Summary of the invention

[0004] The present application provides a method and device for constructing road profile data, which is used to solve the problem that the road profile data in front of the vehicle used as the basis for executing corresponding driving operations in the prior art is small in amount and not rich in content, resulting in low reliability of executing corresponding driving operations and even safety accidents.

[0005] In a first aspect, the present application provides a method for constructing road profile data, which is applied to an on-board controller of a vehicle, and the on-board controller is electrically connected to a point cloud data acquisition module and a posture acquisition module provided on the vehicle, respectively. The method provided by the present application includes:

[0006] Receiving N road section contour point cloud data in a first coordinate system collected at N first moments from a point cloud data collection module, wherein N is an integer greater than or equal to 2, and the distance traveled by the vehicle between two adjacent first moments is equal;

[0007] Determine the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture collection module;

[0008] At each first moment among the N first moments, determine the real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, each historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment;

[0009] At each first moment among the N first moments, converting the road section contour point cloud data in the first coordinate system into the road section contour point cloud data in the second coordinate system according to the real-time posture conversion relationship corresponding to the first moment;

[0010] The road section contour point cloud data in the second coordinate system corresponding to N first moments are spliced ​​to construct the road surface contour point cloud data.

[0011] In a possible implementation, determining the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture collection module includes:

[0012] Receiving real-time posture information in a second coordinate system collected by a posture collection module at multiple second moments, wherein each first moment is between two adjacent second moments;

[0013] For each first moment, the posture information of the vehicle at the first moment in the second coordinate system is determined based on the real-time posture information of the posture acquisition module associated with the previous second moment adjacent to the first moment in the second coordinate system and the real-time posture information of the posture acquisition module associated with the next second moment adjacent to the first moment in the second coordinate system.

[0014] In a possible implementation, for each first moment, determining the posture information of the vehicle at the first moment in the second coordinate system according to the real-time posture information of the posture acquisition module associated with the previous second moment adjacent to the first moment in the second coordinate system and the real-time posture information of the posture acquisition module associated with the next second moment adjacent to the first moment in the second coordinate system, including:

[0015] According to the formula Determine the position information of the vehicle at the first moment in the second coordinate system, where: And θ=acos(q0·q1), t′ is the conversion coefficient, t is the first moment, t0 is the previous second moment adjacent to the first moment; t1 is the next second moment adjacent to the first moment, q0 is the real-time posture information of the posture acquisition module associated with the previous second moment adjacent to the first moment in the second coordinate system, q1 is the real-time posture information of the posture acquisition module associated with the next second moment adjacent to the first moment in the second coordinate system, q t is the position information of the vehicle at the first moment in the second coordinate system, and θ is the angle between q0 and q1.

[0016] In a possible implementation, at each first moment among the N first moments, converting the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system into the road section contour point cloud data in the second coordinate system includes:

[0017] At each of the N first moments, according to the formula The road section contour point cloud data in the first coordinate system is converted into the road section contour point cloud data in the second coordinate system, where T Velo-to-IMU is the real-time posture conversion relationship corresponding to the first moment, The road section contour point cloud data in the first coordinate system at the first moment; It is the road section contour point cloud data in the second coordinate system at the first moment.

[0018] In a possible implementation manner, each location point data in each road segment contour point cloud data includes a collected elevation value.

[0019] In a possible implementation, after receiving N road section contour point cloud data in the first coordinate system collected at N first moments respectively from the point cloud data collection module, the method further includes:

[0020] For each road section contour point cloud data, extract a three-dimensional grid point set with n rows and m columns, where n and m are both integers greater than 2;

[0021] For each grid point in the three-dimensional grid point set, determining a target neighborhood whose distance to the grid point is within a preset distance threshold;

[0022] According to the formula Determine the weight coefficient of each location point in the target neighborhood, where w i is the weight coefficient of the point cloud at the ith position, d i is the distance from the i-th position point cloud in the target neighborhood to the grid point;

[0023] According to the formula Update the elevation value of the grid point, where z pi is the elevation value of the point cloud at the i-th location in the target neighborhood, and Z is the elevation value of the grid point.

[0024] In a possible implementation, for each road segment contour point cloud data, extracting a three-dimensional grid point set with n rows and m columns includes:

[0025] For each road section contour point cloud data, a three-dimensional grid with n rows and m columns is constructed based on the Delaunay triangulation algorithm;

[0026] From the 3D grid, extract a set of 3D grid points with n rows and m columns.

[0027] In a possible implementation, after constructing the road profile point cloud data, the method provided by the present application further includes:

[0028] The road contour point cloud data is rendered to the vehicle's onboard central control screen for display.

[0029] In a possible implementation, after receiving N road section contour point cloud data in the first coordinate system collected at N first moments respectively from the point cloud data collection module, the method further includes:

[0030] Morphological filtering is performed on the N road section contour point cloud data in the first coordinate system.

[0031] In a second aspect, the present application further provides a road profile data construction device, which is configured on a vehicle-mounted controller of a vehicle, and the vehicle-mounted controller is electrically connected to a point cloud data acquisition module and a posture acquisition module provided on the vehicle, respectively, and the device comprises:

[0032] A data receiving unit, used to receive N road section contour point cloud data in a first coordinate system collected by the point cloud data collection module at N first moments, wherein N is an integer greater than or equal to 2, and the distance traveled by the vehicle between two adjacent first moments is equal;

[0033] A posture determination unit, used to determine the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system acquired by the posture acquisition module;

[0034] A conversion relationship determination unit is used to determine, at each first moment among the N first moments, a real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment;

[0035] A data conversion unit, configured to convert, at each first moment among the N first moments, the road section contour point cloud data in the second coordinate system according to the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system;

[0036] The data splicing unit is used to splice the road section contour point cloud data in the second coordinate system corresponding to N first moments to construct the road surface contour point cloud data.

[0037] The present application provides a method and device for constructing road profile data, which determines the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture acquisition module. At each of the N first moments, the real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment is determined according to the posture information of the vehicle at the first moment, the various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system at the starting moment relative to the second coordinate system; at each of the N first moments, the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system are converted into the road section contour point cloud data in the second coordinate system. In this way, the road section contour point cloud data in the second coordinate system corresponding to the N first moments can be spliced ​​to construct the road profile point cloud data. In this way, the constructed road contour point cloud data includes N road section contour point cloud data in the second coordinate system corresponding to the first moment. The amount of road contour point cloud data used as a basis for subsequent execution of corresponding driving operations is richer, denser and broader, making the execution of corresponding driving operations more reliable and reducing the possibility of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A flow chart of a method for constructing road profile data provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the structure of the point cloud data acquisition module and the posture acquisition module provided in the embodiment of the present application located on a vehicle;

[0041] Figure 3 A flowchart for obtaining elevation values ​​provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of a three-dimensional grid point set provided in an embodiment of the present application;

[0043] Figure 5 A schematic diagram of determining a target area with a grid point as the center and a radius of r provided in an embodiment of the present application;

[0044] Figure 6A schematic diagram of the timing relationship between N first moments and N+1 second moments provided in an embodiment of the present application;

[0045] Figure 7 This is a functional module block diagram of the road profile data construction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0047] Various structural schematic diagrams according to embodiments of the present disclosure are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may further design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0048] In the context of the present disclosure, when a layer / element is referred to as being "on" another layer / element, the layer / element may be directly on the other layer / element or an intervening layer / element may exist therebetween. In addition, if a layer / element is "on" another layer / element in one orientation, the layer / element may be "below" the other layer / element when the orientation is reversed.

[0049] The following is a detailed description of the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] See also Figure 1 The embodiment of the present application provides a method for constructing road profile data, which is applied to a vehicle-mounted controller of a vehicle, and the vehicle-mounted controller is electrically connected to a point cloud data acquisition module 201 and a posture acquisition module 202 respectively provided in the vehicle. Figure 2As shown, the point cloud data acquisition module 201 is set in the middle of the top of the vehicle, and the posture acquisition module 202 can be set in the middle of the rear side of the top of the vehicle. Exemplarily, the point cloud data acquisition module 201 can be but not limited to a laser radar, and the posture acquisition module 202 can be but not limited to an inertial navigation module IMU (Inertial Measurement Unit, IMU). It can be understood that when the vehicle is in a driving state, the posture of the vehicle is constantly changing, and the real-time posture information collected by the posture acquisition module 202 is also constantly changing. Specifically, as Figure 1 As shown, the method provided in the embodiment of the present application includes:

[0051] S101: Receive N road section contour point cloud data in a first coordinate system collected at N first moments respectively from the point cloud data collection module 201, where N is an integer greater than or equal to 2.

[0052] Assuming that the N first moments include the first moment t0, the first moment t1, and the first moment t2, the road section contour point cloud data q0 can be collected at the first moment t0, the road section contour point cloud data q1 can be collected at the first moment t1, and the road section contour point cloud data q2 can be collected at the first moment t2. It can be understood that the ground contours of the road sections at different positions are different. Therefore, when the distance traveled by the vehicle between two adjacent first moments is equal, the N road section contour point cloud data can be uniformly collected. It can be understood that when the driving speed of the vehicle is in a dynamic change, the time interval between the first moment t0 and the first moment t1 is different from the time interval between the first moment t1 and the first moment t2.

[0053] In some embodiments, morphological filtering (such as performing corrosion operations and dilation operations) can be performed on the N road section contour point cloud data in the first coordinate system, respectively, which can eliminate small area objects in the road section contour point cloud data, eliminate isolated points, denoise, and smooth the road surface contour, thereby further improving the reliability of the collected N road section contour point cloud data in the first coordinate system.

[0054] In some embodiments, each location point data in each road section contour point cloud data includes the collected elevation value. In this way, the road section contour point cloud data can more realistically describe the road surface contour, for example, potholes or slopes on the road, etc., making the content of the road section contour point cloud data richer and more reliable. Furthermore, the location point cloud with elevation values ​​outside the preset elevation value range in the road section contour point cloud data can be filtered out for preliminary filtering.

[0055] Furthermore, if Figure 3 As shown, the methods for obtaining the elevation value may include:

[0056] S301: For each road segment contour point cloud data, extract a three-dimensional grid point set with n rows and m columns, where n and m are both integers greater than 2.

[0057] Specifically, for each road segment contour point cloud data, a three-dimensional grid with n rows and m columns is constructed based on the Delaunay triangulation algorithm; and a three-dimensional grid point set with n rows and m columns is extracted from the three-dimensional grid. The extracted three-dimensional grid point set can be as follows: Figure 4 shown.

[0058] S302: For each grid point in the three-dimensional grid point set, determine a target neighborhood whose distance to the grid point is within a preset distance threshold.

[0059] like Figure 5 As shown, for each grid point 401, a target area with a radius r taking the grid point 401 as the center can be determined.

[0060] S303: According to the formula Determine the weight coefficient for each location in the target neighborhood.

[0061] Among them, w i is the weight coefficient of the point cloud at the ith position, d i is the distance from the i-th position point cloud in the target neighborhood to the grid point.

[0062] Specifically, according to the formula Determine the distance from the i-th position point cloud in the target neighborhood to the grid point, where (x i ,y i ) is the coordinate of the ith grid point, and (x, y) is the coordinate of a position point in the target neighborhood.

[0063] S304: According to the formula Update the elevation value of the grid point, where z pi is the elevation value of the point cloud at the i-th location in the target neighborhood, and Z is the elevation value of the grid point.

[0064] S102: Determine the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture collection module 202.

[0065] It can be understood that the N second moments of the real-time posture information of the vehicle in the second coordinate system collected by the posture acquisition module 202 may be the same as or different from the above-mentioned N first moments. When the N second moments of the real-time posture information of the vehicle in the second coordinate system collected by the posture acquisition module 202 are different from the above-mentioned N first moments, S102 can be specifically implemented as follows:

[0066] Step 1: Receive real-time posture information in a second coordinate system collected by the posture collection module 202 at multiple second moments, wherein each first moment is between two adjacent second moments.

[0067] like Figure 6 As shown, it is assumed that the N first moments include the first moment t0, the first moment t1 and the first moment t2; the N+1 second moments include the second moment ta, the second moment tb, the second moment tc and the second moment td, and the first moment t0 is located between the second moment ta and the second moment tb, the first moment t1 is located between the second moment tb and the second moment tc, and the first moment t2 is located between the second moment tc and the second moment td.

[0068] Step 2: For each first moment, determine the posture information of the vehicle at the first moment in the second coordinate system based on the real-time posture information of the posture acquisition module 202 in the second coordinate system associated with the previous second moment adjacent to the first moment and the real-time posture information of the posture acquisition module 202 in the second coordinate system associated with the next second moment adjacent to the first moment.

[0069] Specifically, according to the formula Determine the position and posture information of the vehicle at the first moment in the second coordinate system.

[0070] in, And θ=acos(q0·q1), θ is the angle between q0 and q1, t′ is the conversion coefficient, t is the first moment, t0 is the previous second moment adjacent to the first moment; t1 is the next second moment adjacent to the first moment, q0 is the real-time posture information of the posture acquisition module 202 associated with the previous second moment adjacent to the first moment in the second coordinate system, q1 is the real-time posture information of the posture acquisition module 202 associated with the next second moment adjacent to the first moment in the second coordinate system; q t is the position information of the vehicle at the first moment in the second coordinate system.

[0071] For example, Figure 6 As shown, for the first moment t1, the first moment t1 is located at the second moment t b (i.e. the second time t0) and the second time t c (i.e., the second moment t1), the second moment t b The pose information is q b (i.e., q0); the position information of the vehicle at the second moment tc in the second coordinate system is q c (i.e. q1), then θ=acos(q b ·q c); then, the position information of the vehicle at the first moment in the second coordinate system

[0072] S103: At each of the N first moments, determine the real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, the historical posture information of the posture acquisition module 202 in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment.

[0073] Exemplarily, the real-time posture transformation relationship of the first coordinate system relative to the second coordinate system at the first moment can be determined by integration based on the posture information of the vehicle at the first moment, the historical posture information of the posture acquisition module 202 in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture transformation relationship of the first coordinate system relative to the second coordinate system at the starting moment.

[0074] S104: At each first moment among the N first moments, converting the road section contour point cloud data in the first coordinate system into the road section contour point cloud data in the second coordinate system according to the real-time posture conversion relationship corresponding to the first moment.

[0075] In some embodiments, at each of the N first moments, according to the formula The road section contour point cloud data in the first coordinate system is converted into the road section contour point cloud data in the second coordinate system. Velo-to-IMU is the real-time posture conversion relationship corresponding to the first moment, The road section contour point cloud data in the first coordinate system at the first moment; It is the road section contour point cloud data in the second coordinate system at the first moment.

[0076] For example, Figure 6 As shown, the real-time posture conversion relationship corresponding to the first moment t1 is T Velo-to-IMU1 , and the road section contour point cloud data in the first coordinate system collected at the first time t1 is When , we can use the formula The road section contour point cloud data in the first coordinate system is converted into the road section contour point cloud data in the second coordinate system. is the road section contour point cloud data in the first coordinate system at the first moment t1, is the road section contour point cloud data in the second coordinate system collected at the first time t1, T Velo-to-IMU1 It is the real-time posture conversion relationship corresponding to the first moment t1.

[0077] In other embodiments, it is assumed that the N first moments include the first moment t0, the first moment t1, and the first moment t2. At the first moment t1, the posture conversion relationship T of the posture acquisition module 202 at the first moment t0 relative to the first moment t1 can be determined according to the real-time posture conversion relationship corresponding to the first moment t0 and the real-time posture conversion relationship at the first moment t1. 0-1 , and the real-time posture conversion relationship corresponding to the first moment t0 is T Velo-to-IMU0 , and the road section contour point cloud data in the first coordinate system at the first moment t1 is At the first moment t1, the road section contour point cloud data in the second coordinate system is Similarly, at the first moment t2, the posture conversion relationship T of the posture acquisition module 202 at the first moment t2 relative to the first moment t1 can be determined according to the real-time posture conversion relationship corresponding to the first moment t1 and the real-time posture conversion relationship at the first moment t2. 1-2 , and the real-time posture conversion relationship corresponding to the first moment t0 is T Velo-to-IMU0 T 0-1 , and at the first moment t2, the road section contour point cloud data in the first coordinate system is When , the road section contour point cloud data in the second coordinate system at the first moment t2 is

[0078] S105: splicing the N road section contour point cloud data corresponding to the first moment in the second coordinate system to construct road surface contour point cloud data.

[0079] For example, when the N first moments include the first moment t0, the first moment t1 and the first moment t2, the road section contour point cloud data in the second coordinate system corresponding to the first moment t0, the road section contour point cloud data in the second coordinate system corresponding to the first moment t1, and the road section contour point cloud data in the second coordinate system corresponding to the first moment t2 can be spliced ​​to construct the road surface contour point cloud data.

[0080] In some embodiments, a three-dimensional grid point set of n rows and m columns (each grid point may include an elevation value) can be extracted from the road section contour point cloud data in the second coordinate system corresponding to each first moment, and the n rows and m columns of the three-dimensional grid point set at each moment can be spliced ​​to obtain the road surface contour point cloud data with high efficiency.

[0081] In summary, the road profile data construction method provided in the embodiment of the present application determines the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture acquisition module. At each of the N first moments, the real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment is determined according to the posture information of the vehicle at the first moment, the various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system at the starting moment relative to the second coordinate system; at each of the N first moments, the real-time posture conversion relationship corresponding to the first moment and the road profile point cloud data in the first coordinate system are converted into the road profile point cloud data in the second coordinate system. In this way, the road profile point cloud data in the second coordinate system corresponding to the N first moments can be spliced ​​to construct the road profile point cloud data. In this way, the constructed road contour point cloud data includes N road section contour point cloud data in the second coordinate system corresponding to the first moment. The road contour point cloud data used as a basis for subsequent execution of corresponding driving operations is richer, denser and broader, making the execution of corresponding driving operations more reliable and reducing the possibility of safety accidents.

[0082] In addition, the method provided in the embodiment of the present application can also render the road contour point cloud data to the vehicle's on-board central control screen for display, which can further enhance the user's interactive experience.

[0083] like Figure 7 As shown, the embodiment of the present application also provides a road surface contour data construction device 700, which is configured on the vehicle's onboard controller, and the onboard controller is electrically connected to the point cloud data acquisition module and the posture acquisition module arranged on the vehicle. It should be noted that the basic principle and technical effect of the road surface contour data construction device 700 provided in the embodiment of the present application are the same as those in the above-mentioned embodiment. For the sake of brief description, for parts not mentioned in the embodiment of the present application, reference can be made to the corresponding contents in the above-mentioned embodiment. The device 700 provided in the embodiment of the present application includes a data receiving unit 701, a posture determination unit 702, a conversion relationship determination unit 703, a data conversion unit 704, and a data splicing unit 705, wherein,

[0084] The data receiving unit 701 is used to receive N road section contour point cloud data in the first coordinate system collected by the point cloud data collection module at N first moments, wherein N is an integer greater than or equal to 2, and the distance traveled by the vehicle between two adjacent first moments is equal;

[0085] A posture determination unit 702, configured to determine the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system acquired by the posture acquisition module;

[0086] The conversion relationship determination unit 703 is used to determine, at each first moment among the N first moments, the real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, the various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment;

[0087] The data conversion unit 704 is used to convert the road section contour point cloud data in the second coordinate system at each first moment among the N first moments according to the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system.

[0088] The data splicing unit 705 is used to splice the road section contour point cloud data in the second coordinate system corresponding to N first moments to construct road surface contour point cloud data.

[0089] In some embodiments, the posture determination unit 702 is specifically used to receive real-time posture information in the second coordinate system collected by the posture acquisition module at multiple second moments, wherein each first moment is between two adjacent second moments; for each first moment, the posture information of the vehicle at the first moment in the second coordinate system is determined based on the real-time posture information in the second coordinate system of the posture acquisition module associated with the previous second moment adjacent to the first moment and the real-time posture information in the second coordinate system of the posture acquisition module associated with the next second moment adjacent to the first moment.

[0090] In some embodiments, the posture determination unit 702 is further specifically configured to determine the position of the posture according to the formula Determine the position and posture information of the vehicle at the first moment in the second coordinate system.

[0091] in, And θ=acos(q0·q1), t′ is the conversion coefficient, t is the first moment, t0 is the previous second moment adjacent to the first moment; t1 is the next second moment adjacent to the first moment, q0 is the real-time posture information of the posture acquisition module associated with the previous second moment adjacent to the first moment in the second coordinate system, q1 is the real-time posture information of the posture acquisition module associated with the next second moment adjacent to the first moment in the second coordinate system, θ is the angle between q0 and q1, q t is the position information of the vehicle at the first moment in the second coordinate system.

[0092] In some implementations, the data conversion unit 704 is specifically configured to convert, at each first moment in the N first moments, The road section contour point cloud data in the first coordinate system is converted into the road section contour point cloud data in the second coordinate system, where T Velo-to-IMU is the real-time posture conversion relationship corresponding to the first moment, The road section contour point cloud data in the first coordinate system at the first moment; It is the road section contour point cloud data in the second coordinate system at the first moment.

[0093] In some implementations, each location point data in each road segment contour point cloud data includes a collected elevation value.

[0094] In some implementations, the apparatus 700 provided in the embodiments of the present application further includes:

[0095] The elevation value determination unit is used to extract a three-dimensional grid point set with n rows and m columns for each road section contour point cloud data, wherein n and m are both integers greater than 2; for each grid point in the three-dimensional grid point set, determine a target neighborhood whose distance to the grid point is within a preset distance threshold; according to the formula Determine the weight coefficient of each location point in the target neighborhood, where w i is the weight coefficient of the point cloud at the ith position, d i is the distance from the i-th position point cloud in the target neighborhood to the grid point; according to the formula Update the elevation value of the grid point, where z pi is the elevation value of the point cloud at the i-th location in the target neighborhood, and Z is the elevation value of the grid point.

[0096] Furthermore, the elevation value determination unit is specifically used to construct a three-dimensional grid with n rows and m columns for each road section contour point cloud data based on the Delaunay triangulation algorithm; and extract a three-dimensional grid point set with n rows and m columns from the three-dimensional grid.

[0097] In some embodiments, the device 700 provided in the embodiment of the present application may further include: a data rendering unit, used to render the road contour point cloud data to the vehicle's on-board central control screen for display.

[0098] In some implementations, the apparatus provided by the embodiments of the present application may further include: a data filtering unit, configured to perform morphological filtering on N road segment contour point cloud data in the first coordinate system.

[0099] In the above description, the technical details such as the patterning of each layer are not described in detail. However, those skilled in the art should understand that various technical means can be used to form layers, regions, etc. of desired shapes. In addition, in order to form the same structure, those skilled in the art can also design methods that are not completely the same as the methods described above. In addition, although the various embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage.

[0100] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0101] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for constructing road profile data, It is characterized in that An on-board controller applied to a vehicle, the on-board controller being electrically connected to a point cloud data acquisition module and a posture acquisition module respectively arranged on the vehicle, the method comprising: Receiving N road section contour point cloud data in a first coordinate system collected by the point cloud data collection module at N first moments, wherein N is an integer greater than or equal to 2, and the distance traveled by the vehicle between two adjacent first moments is equal; Determine the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture collection module; At each of the N first moments, determine a real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and the initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment; At each of the N first moments, converting the road section contour point cloud data in the first coordinate system into the road section contour point cloud data in the second coordinate system according to the real-time posture conversion relationship corresponding to the first moment; The N road section contour point cloud data corresponding to the first moment in the second coordinate system are spliced ​​to construct road surface contour point cloud data.

2. The method according to claim 1, It is characterized in that The determining the posture information of the vehicle at each first moment in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system collected by the posture collection module includes: Receiving real-time posture information in a second coordinate system collected by the posture collection module at multiple second moments, wherein each of the first moments is between two adjacent second moments; For each first moment, the posture information of the vehicle at the first moment in the second coordinate system is determined based on the real-time posture information of the posture acquisition module in the second coordinate system associated with the previous second moment adjacent to the first moment and the real-time posture information of the posture acquisition module in the second coordinate system associated with the next second moment adjacent to the first moment.

3. The method according to claim 2, It is characterized in that The method of determining the posture information of the vehicle at the first moment in the second coordinate system according to the real-time posture information of the posture acquisition module in the second coordinate system associated with the previous second moment adjacent to the first moment and the real-time posture information of the posture acquisition module in the second coordinate system associated with the next second moment adjacent to the first moment for each first moment, comprises: According to the formula Determining the position information of the vehicle at the first moment in the second coordinate system; in, And θ=acos(q 0 ·q 1 ), t′ is the conversion coefficient, t is the first moment, t 0 is the previous second moment adjacent to the first moment; t 1 is the second moment adjacent to the first moment, q 0 is the real-time posture information of the posture acquisition module in the second coordinate system associated with the previous second moment adjacent to the first moment, 1 is the real-time posture information of the posture acquisition module in the second coordinate system associated with the next second moment adjacent to the first moment, θ is q 0 With q 1 The angle between t is the position information of the vehicle at the first moment in the second coordinate system.

4. The method according to claim 1, It is characterized in that The converting, at each of the N first moments, into the road section contour point cloud data in the second coordinate system according to the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system, comprises: At each of the N first moments, according to the formula The road segment contour point cloud data in the first coordinate system is converted into the road segment contour point cloud data in the second coordinate system, wherein T Velo-to-IMU is the real-time posture conversion relationship corresponding to the first moment, The road section contour point cloud data in the first coordinate system at the first moment; It is the road section contour point cloud data in the second coordinate system at the first moment.

5. The method according to claim 1, It is characterized in that Each location point data in each of the road section contour point cloud data includes a collected elevation value.

6. The method according to claim 5, It is characterized in that After receiving N road section contour point cloud data in the first coordinate system collected at N first moments respectively from the point cloud data collection module, the method further includes: For each road section contour point cloud data, extract a three-dimensional grid point set with n rows and m columns, where n and m are both integers greater than 2; For each grid point in the three-dimensional grid point set, determining a target neighborhood whose distance to the grid point is within a preset distance threshold; According to the formula Determine the weight coefficient of each location point in the target neighborhood, where w i is the weight coefficient of the point cloud at the ith position, d i is the distance from the i-th position point cloud in the target neighborhood to the grid point; According to the formula Update the elevation value of the grid point, where is the elevation value of the i-th position point cloud in the target neighborhood, and Z is the elevation value of the grid point.

7. The method according to claim 6, It is characterized in that For each road section contour point cloud data, extracting a three-dimensional grid point set with n rows and m columns includes: For each road section contour point cloud data, a three-dimensional grid with n rows and m columns is constructed based on the Delaunay triangulation algorithm; A three-dimensional grid point set with n rows and m columns is extracted from the three-dimensional grid.

8. The method according to any one of claims 1 to 7, It is characterized in that After constructing the road profile point cloud data, the method further includes: The road contour point cloud data is rendered and displayed on the vehicle's onboard central control screen.

9. The method according to any one of claims 1 to 7, It is characterized in that After receiving N road section contour point cloud data in the first coordinate system collected at N first moments respectively from the point cloud data collection module, the method further includes: Morphological filtering is performed on the N road section contour point cloud data in the first coordinate system.

10. A road profile data construction device, It is characterized in that An on-board controller configured on a vehicle, the on-board controller is electrically connected to a point cloud data acquisition module and a posture acquisition module respectively arranged on the vehicle, and the device comprises: A data receiving unit, used to receive N road section contour point cloud data in a first coordinate system collected by the point cloud data collection module at N first moments, wherein N is an integer greater than or equal to 2, and the distance traveled by the vehicle between two adjacent first moments is equal; A posture determination unit, configured to determine the posture information of the vehicle at each of the first moments in the second coordinate system according to the real-time posture information of the vehicle in the second coordinate system acquired by the posture acquisition module; A conversion relationship determination unit is used to determine, at each of the N first moments, a real-time posture conversion relationship of the first coordinate system relative to the second coordinate system at the first moment according to the posture information of the vehicle at the first moment, various historical posture information of the posture acquisition module in the second coordinate system between the pre-recorded starting moment and the first moment, and an initial posture conversion relationship of the first coordinate system relative to the second coordinate system at the starting moment; A data conversion unit, configured to convert, at each of the N first moments, the real-time posture conversion relationship corresponding to the first moment and the road section contour point cloud data in the first coordinate system into the road section contour point cloud data in the second coordinate system; The data splicing unit is used to splice the N road section contour point cloud data in the second coordinate system corresponding to the first moment to construct road surface contour point cloud data.

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