Mapping and Localization Method, Device, Equipment and Storage Medium

Abstract

The present application provides a mapping and positioning method, device, equipment and storage medium. The present application collects radar data and GNSS / RTK data and performs data preprocessing; initial radar weight factors and GNSS / RTK weight factors are respectively set according to the matching degree of point clouds at adjacent times and the signal strength of GNSS / RTK data; based on the coordinate and distribution information of the point clouds, the two weight factors are adjusted and then normalized to obtain the final radar weight factors and GNSS / RTK weight factors; a map is established according to the final two weight factors. By setting the radar weight factors and GNSS / RTK weight factors and adjusting the two weight factors based on the coordinate and distribution information of the point clouds, the present application realizes the effective fusion of radar data and GNSS / RTK data, realizes the joint mapping of radar technology and GNSS / RTK technology, and ensures the accuracy of the global map.

Description

Technical Field

[0001] This application relates to the technical field of mapping and positioning, and in particular, to a mapping and positioning method, device, equipment, and storage medium. Background Art

[0002] With the development of artificial intelligence technology, applications such as mobile robots, unmanned driving, and autonomous driving are becoming more and more widespread. Among them, during the process of robots, cars, etc. moving in a dynamic unknown environment and autonomously completing tasks, it is necessary to select a relatively safe and reasonable route for movement, such as avoiding collisions with buildings, and choosing a route with a shorter mileage or a flatter surface.

[0003] Currently, mapping and positioning technologies include lidar mapping and positioning technology, GNSS (Global Navigation Satellite System) mapping and positioning technology, and RTK (Real-time kinematic) mapping and positioning technology. The process of mapping and positioning is as follows: During the movement of the device, self-positioning is performed based on position estimation and sensor data, and at the same time, a complete map is gradually improved and constructed. The lidar mapping technology emits scanning lasers to the target through the lidar and receives the lasers reflected by the target for detection to obtain parameters such as the position and shape of the object. Both GNSS mapping and positioning technology or RTK mapping and positioning technology (hereinafter referred to as GNSS / RTK mapping and positioning technology) obtain position information by receiving electromagnetic wave signals sent by global navigation satellites through a ground base station.

[0004] However, the above-mentioned lidar mapping and positioning technology needs to process a large amount of raw data and is prone to cumulative errors during large-scale mapping; the above-mentioned GNSS / RTK mapping and positioning technology is easily affected by obstacles. None of the above technologies can guarantee the accuracy of the global map during large-scale mapping. Summary of the Invention

[0005] This application provides a mapping and positioning method, device, equipment, and storage medium to ensure the accuracy of the global map during large-scale mapping.

[0006] In a first aspect, the present application provides a mapping and positioning method, comprising: obtaining radar data collected by a radar sensor, and GNSS / RTK data collected by a global navigation satellite system GNSS or a real-time carrier phase differential RTK sensor; performing time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different times; setting an initial radar weight factor and a GNSS / RTK weight factor according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data at adjacent times; and obtaining the degree of openness around the current position based on the coordinates and distribution information of the point cloud. The initial radar weight factor and the initial GNSS / RTK weight factor are adjusted according to the degree of openness around the current position to obtain adjusted radar weight factor and adjusted GNSS / RTK weight factor; wherein the point cloud is established based on radar data; according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, adjustment processing is performed again to obtain final radar weight factor and GNSS / RTK weight factor; wherein the sum of the final radar weight factor and GNSS / RTK weight factor is 1; according to the final radar weight factor and GNSS / RTK weight factor, a map is established using a mapping algorithm.

[0007] This application obtains both radar data and GNSS / RTK data. By determining the weight factor of the two, the joint mapping of radar technology and GNSS / RTK technology is realized, so that the accuracy of the global map is guaranteed when mapping on a large scale. The two technologies complement each other. For example, when the credibility of radar data in an open area is low, GNSS / RTK technology can ensure the accuracy of the global map; for example, when the credibility of GNSS / RTK data in a densely built area is low, radar technology can ensure the accuracy of the global map.

[0008] Furthermore, before setting the initial radar weight factor and the GNSS / RTK weight factor according to the matching degree of the point clouds at adjacent moments and the signal strength of the GNSS / RTK data at different moments, the method further includes: performing coordinate conversion processing on the radar data to obtain the radar data in the geographic coordinate system.

[0009] The above method puts radar data and GNSS / RTK data in the same coordinate system, so that radar data can be used for mapping and positioning. Since radar data is data with its own sensor as the origin coordinate, converting radar data to the geographic coordinate system is conducive to directly using radar data for mapping and positioning.

[0010] Furthermore, according to the matching degree of the point clouds at adjacent moments, the initial radar weight factor is set, including:

[0011] Based on the radar data at adjacent moments, obtain the point cloud at adjacent moments;

[0012] According to the point cloud at adjacent moments, calculate and obtain the initial radar weight factor based on the first formula; where the first formula includes:

[0013]

[0014] where, GB Lidar is the initial radar weight factor; P match is the number of successfully matched points between the point clouds at adjacent moments, P sum is the number of points in the point cloud at the latter moment among adjacent moments, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

[0015] The above formula provides a feasible method for obtaining the initial radar weight factor. According to the matching degree of two adjacent frames of point clouds, determine the radar weight factor to characterize the credibility of the radar data.

[0016] Furthermore, set the initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data, including: calculate and obtain the initial GNSS / RTK weight factor based on the second formula according to the signal strength of the GNSS / RTK data; where the second formula includes:

[0017] GA GNSS / RTK =k×(∑(P i -P A ) / ∑(P j -P A ))

[0018] where, GA GNSS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th satellite captured to meet the signal strength; P A is the signal strength required to capture the satellite; P j is the signal strength when all satellite signals are theoretically the strongest; k is a predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

[0019] The above formula provides a feasible method for obtaining the initial GNSS / RTK weight factor. According to the GNSS / RTK data signal strength, determine the GNSS / RTK weight factor to characterize the credibility of the GNSS / RTK data.

[0020] Further, based on the coordinates and distribution information of the point cloud, the degree of openness around the current position is obtained, and the initial radar weight factor and the initial GNSS / RTK weight factor are adjusted according to the degree of openness to obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, including:

[0021] According to the coordinates and distribution information of the point cloud, based on the third formula, the adjusted GNSS / RTK weight factor is calculated; wherein the third formula includes:

[0022]

[0023] Among them, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the point cloud p i The cosine value of the angle between the line connecting the point to the radar sensor and the vertical upward direction of the ground;

[0024] According to the coordinates and distribution information of the point cloud, based on the fourth formula, the adjusted radar weight factor is calculated; wherein the fourth formula includes:

[0025]

[0026] Among them, GC Lidar is the adjusted radar weight factor; is the number of points in the point cloud that belong to the plane; is the number of points in the point cloud that belong to the edge; is the number of corner points in the point cloud; Sum P is the total number of points in the point cloud; a, b and c are the weight coefficients of plane features, edge features and corner features respectively.

[0027] The above method adjusts the initial radar weight factor and the initial GNSS / RTK weight factor according to the openness of the area. When the openness is high, the accuracy of radar mapping and positioning technology is low, the radar weight factor is reduced, and the GNSS / RTK weight factor is increased, so that GNSS / RTK mapping and positioning is mainly used in open areas; when the openness is low, the accuracy of GNSS / RTK mapping and positioning technology is low, the radar weight factor is increased, and the GNSS / RTK weight factor is reduced, so that radar mapping and positioning is mainly used in areas with dense obstacles.

[0028] Further, the adjusted radar weight factor and the adjusted GNSS / RTK weight factor are adjusted again to obtain the final radar weight factor and the GNSS / RTK weight factor, including:

[0029] Based on the fifth formula, the final radar weight factor and GNSS / RTK weight factor are obtained; wherein, the fifth formula includes:

[0030]

[0031]

[0032] Wherein, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

[0033] The above re - adjustment of the radar weight factor and GNSS / RTK weight factor makes the sum of their weights equal to 1, which is conducive to comparison and also facilitates mapping.

[0034] Furthermore, the above mapping and positioning method further includes: obtaining the distance between the moving object and the building;

[0035] According to the signal strength of the GNSS / RTK data, set the initial GNSS / RTK weight factor, specifically including: if the distance is less than a preset first threshold, then set the initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; wherein, the initial GNSS / RTK weight factor is not greater than the preset first threshold;

[0036] According to the matching degree of the point cloud at adjacent moments, set the initial radar weight factor, specifically including: if the distance exceeds a preset second threshold, then set the initial radar weight factor according to the matching degree of the point cloud at adjacent moments; wherein, the initial radar weight factor is not greater than the preset second threshold.

[0037] In the above method, during the process of combined mapping and positioning of radar and GNSS / RTK, by analyzing the changes in the radar weight factor and GNSS / RTK weight factor, the path is reasonably planned to achieve mapping and positioning in the best state of the sensor.

[0038] Next, the devices, equipment, and computer storage media provided by the present application are introduced, and their content and effects can be referred to the method part.

[0039] In the second aspect, the present application provides a mapping and positioning device, including: an acquisition module for acquiring radar data collected by a radar sensor and GNSS / RTK data collected by a GNSS / RTK sensor; a processing module for performing time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different times; the processing module is also used to set an initial radar weight factor and a GNSS / RTK weight factor according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data at adjacent times, respectively; and, based on the coordinates and distribution information of the point cloud, obtain the degree of openness around the current position and calculate the distance between the current position and the surrounding area according to the current position. The initial radar weight factor and the initial GNSS / RTK weight factor are adjusted according to the degree of openness around the location to obtain adjusted radar weight factor and adjusted GNSS / RTK weight factor; wherein the point cloud is established based on radar data; the processing module is further used to adjust and process again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain final radar weight factor and GNSS / RTK weight factor; wherein the sum of the final radar weight factor and GNSS / RTK weight factor is 1; the mapping module is used to establish a map using a mapping algorithm according to the final radar weight factor and GNSS / RTK weight factor.

[0040] Furthermore, the processing module is used to perform coordinate conversion processing on the radar data before setting the initial radar weight factor and GNSS / RTK weight factor according to the matching degree of the point clouds at adjacent time moments and the signal strength of the GNSS / RTK data at different time moments, so as to obtain the radar data in the geographic coordinate system.

[0041] Furthermore, the processing module is used to set the initial radar weight factor according to the matching degree of the point clouds at adjacent moments, specifically including:

[0042] Based on the radar data at adjacent moments, point clouds at adjacent moments are obtained;

[0043] According to the point clouds at adjacent moments, the initial radar weight factor is calculated based on the first formula; wherein the first formula includes:

[0044]

[0045] Among them, GB Lidar is the initial radar weight factor; P match is the number of points that are successfully matched between point clouds at adjacent moments, P sum is the number of points in the point cloud at the next moment in the adjacent moments, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

[0046] Furthermore, the processing module is used to set an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data, specifically including:

[0047] According to the signal strength of the GNSS / RTK data, an initial GNSS / RTK weight factor is calculated based on a second formula; wherein the second formula includes:

[0048] GA GNSS / RTK =k×(∑(P i -P A ) / ∑(P j -P A ))

[0049] Among them, GA GNSS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th star that meets the signal strength requirement; P A is the signal strength required to capture the satellite; P j is the signal strength when all satellite signals are theoretically the strongest; k is the predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

[0050] Further, the processing module is used to obtain the degree of openness around the current position based on the coordinates and distribution information of the point cloud, and adjust the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness to obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, specifically including:

[0051] According to the coordinates and distribution information of the point cloud, based on the third formula, the adjusted GNSS / RTK weight factor is calculated; wherein the third formula includes:

[0052]

[0053] Among them, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the point cloud p i The cosine value of the angle between the line connecting the point to the radar sensor and the vertical upward direction of the ground;

[0054] According to the coordinates and distribution information of the point cloud, based on the fourth formula, the adjusted radar weight factor is calculated; wherein the fourth formula includes:

[0055]

[0056] Among them, GC Lidar is the adjusted radar weight factor; is the number of points in the point cloud that belong to the plane; is the number of points in the point cloud that belong to the edge; is the number of points in the point cloud that belong to the corner point; Sum P is the total number of points in the point cloud; a, b, and c are the weight coefficients of the plane feature, edge feature, and corner point feature, respectively.

[0057] Furthermore, a processing module is configured to perform adjustment processing again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain the final radar weight factor and GNSS / RTK weight factor, specifically including:

[0058] Based on the fifth formula, obtain the final radar weight factor and GNSS / RTK weight factor; wherein, the fifth formula includes:

[0059]

[0060]

[0061] wherein, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

[0062] Furthermore, the device further includes: a navigation module configured to obtain the distance between the moving object and the building;

[0063] A processing module is configured to set an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data, specifically including: if the distance is less than a preset first threshold, set the initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; wherein, the initial GNSS / RTK weight factor is not greater than the preset first threshold;

[0064] A processing module is configured to set an initial radar weight factor according to the matching degree of the point cloud at adjacent moments, specifically including: if the distance exceeds a preset second threshold, set the initial radar weight factor according to the matching degree of the point cloud at adjacent moments; wherein, the initial radar weight factor is not greater than the preset second threshold.

[0065] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method as in the first aspect.

[0066] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method according to the first aspect when executed by a processor.

[0067] The mapping and positioning method, device, equipment and storage medium provided by the present application perform time synchronization processing and coordinate transformation processing on the radar data collected by the radar sensor and the GNSS / RTK data collected by the GNSS / RTK sensor; respectively set initial radar weight factors and GNSS / RTK weight factors according to the matching degree of point clouds and the signal strength of GNSS / RTK data at adjacent moments; based on the coordinate and distribution information of the point clouds, adjust the initial radar weight factors and GNSS / RTK weight factors, and then perform normalization processing to obtain the final radar weight factors and GNSS / RTK weight factors; establish a map using a mapping algorithm according to the final radar weight factors and GNSS / RTK weight factors. The present application sets radar weight factors and GNSS / RTK weight factors, and adjusts the radar weight factors and GNSS / RTK weight factors based on the coordinate and distribution information of the point clouds, realizing the combined utilization of radar technology and GNSS / RTK technology, and ensuring the accuracy of the global map during large-scale mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0069] Figure 1 is an application scenario of a mapping and positioning method provided by the present application;

[0070] Figure 2 is a flowchart of a mapping and positioning method provided in Embodiment 1 of the present application;

[0071] Figure 3 is a flowchart of another mapping and positioning method provided in Embodiment 1 of the present application;

[0072] Figure 4 is a schematic structural diagram of a mapping and positioning device provided in Embodiment 2 of the present application;

[0073] Figure 5 is a schematic structural diagram of another mapping and positioning device provided in Embodiment 2 of the present application;

[0074] Figure 6 is a schematic structural diagram of an electronic device provided in Embodiment 3 of the present application.

[0075] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and will be described in more detail hereinafter. These drawings and the written description are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0076] Here, exemplary embodiments will be described in detail, and examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application.

[0077] First, the terms related to the present application will be explained:

[0078] Point cloud: It refers to a massive set of points representing the surface characteristics of an object. When a laser beam irradiates an object surface, the reflected laser beam will carry information such as azimuth and distance. If the laser beam is scanned along a certain trajectory, the information of the reflected laser points will be recorded while scanning. Since the scanning is extremely fine, a large number of laser points can be obtained, thus forming a laser point cloud.

[0079] Point cloud matching: The purpose of image registration is to compare or fuse. For images of the same object obtained under different conditions, due to the occlusion of the laser scanning beam by the object, it is impossible to obtain the three-dimensional point cloud of the entire object through a single scan. Therefore, it is necessary to scan the object from different positions and angles. The point clouds at different positions are transformed to the same position through the information of the overlapping part to achieve the splicing of point cloud data.

[0080] ICP (Iterative Closest Point) algorithm: An algorithm based on the data registration method, using the nearest point search method to solve the registration problem based on free-form surfaces.

[0081] LiDAR (Light Detection and Ranging): Generally called lidar, it is an active measurement method, mainly composed of a laser emission part, a receiving part, and a signal processing part. It has two main basic functions: ranging and detection.

[0082] GNSS (Global Navigation Satellite System): A basic radio navigation and positioning system that can provide users with three-dimensional coordinates, speed, and time information all-weather at any location on the earth's surface or in near-earth space.

[0083] RTK (Real Time Kinematic) technology: It adopts carrier phase differential technology, which is a differential method for real-time processing of carrier phase observations at two measurement stations. The carrier phase collected by the reference station is sent to the user receiver for differential solution to calculate coordinates.

[0084] It should be noted that "GNSS / RTK" in the following text refers to GNSS or RTK. For example, "GNSS / RTK mapping and positioning technology" refers to GNSS mapping and positioning technology or RTK mapping and positioning technology; "GNSS / RTK sensor" refers to GNSS sensor or RTK sensor. When using GNSS mapping and positioning technology, GNSS sensors are correspondingly used to receive GNSS positioning data; when using RTK mapping and positioning technology, RTK sensors are correspondingly used to receive RTK positioning data, and so on. Other descriptions of "GNSS / RTK" all refer to GNSS or RTK.

[0085] With the development of artificial intelligence technology, applications such as mobile robots, unmanned driving, and autonomous driving are becoming more and more widespread. Among them, during the process of robots, cars, etc. moving in a dynamic unknown environment and autonomously completing tasks, it is necessary to select a relatively safe and reasonable route for movement, such as avoiding collisions with buildings and choosing a route with a shorter mileage or a flatter surface.

[0086] Currently, mapping and positioning technologies include lidar mapping and positioning technology, GNSS / RTK mapping and positioning technology. The process of mapping and positioning is as follows: During movement, the device performs self-positioning based on position estimation and sensor data, and gradually improves and constructs a complete map at the same time. The lidar mapping technology emits scanning lasers to the target through the lidar and receives the lasers reflected by the target for detection to obtain parameters such as the position and shape of the object. The GNSS / RTK mapping and positioning technology receives electromagnetic wave signals sent by global navigation satellites through a ground base station to obtain position information.

[0087] However, the above-mentioned lidar mapping and positioning technology needs to process a large amount of raw data and is prone to cumulative errors during large-scale mapping; the above-mentioned GNSS / RTK mapping and positioning technology is easily affected by obstacles. None of the above technologies can guarantee the accuracy of the global map during large-scale mapping.

[0088] This application provides a mapping and positioning method, device, equipment, and storage medium, aiming to solve the above technical problems in the prior art.

[0089] This application can be applied to scenarios such as the navigation of autonomous vehicles, such as Figure 1As shown in the figure. The driverless vehicle is equipped with a mapping and positioning device, which scans the surrounding environment of the vehicle, constructs a map, and identifies its own position. As the vehicle moves continuously, the device scans the surrounding environment in real time, obtains environmental data from different angles, and continuously improves the map. By analyzing the environmental data, the device can guide the vehicle to avoid obstacles and plan the driving route. Specifically, as Figure 1 The mapping and positioning device shown in the figure includes a lidar sensor and a GNSS / RTK sensor. Among them, the lidar sensor can receive the reflected laser of the laser emitted by the lidar, and the reflected laser is reflected by an obstacle. The lidar sensor detects the distance between the obstacle and the vehicle through the time difference, changes the driving route of the vehicle, and avoids collisions. The GNSS / RTK sensor can receive the electromagnetic waves emitted by GNSS navigation satellites, determine its own geographical location, and combine the carrier phase differential technology (RTK) to obtain positioning data with an accuracy of centimeter units.

[0090] In the actual application process, the lidar can be replaced by a 4D millimeter-wave radar or a binocular stereo vision device. This application does not make any limitations in this regard.

[0091] The following will specifically describe the technical solutions of this application and how the technical solutions of this application solve the above technical problems through specific embodiments. These 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 this application will be described below with reference to the accompanying drawings.

[0092] Embodiment 1

[0093] Figure 2 The following is a flowchart of a mapping and positioning method provided in Embodiment 1 of this application, including the following steps:

[0094] S101. Obtain the radar data collected by the radar sensor and the GNSS / RTK data collected by the GNSS / RTK sensor.

[0095] In the actual application process, the radar can be a traditional microwave radar or a lidar, and can be selected according to the accuracy requirements. This application does not make any limitations. Specifically, a lidar is a radar system that detects the position, speed, and other characteristic quantities of a target by emitting laser beams. It combines technologies such as light sources and photoelectric detection, and sometimes includes computer image processing technology, and can simultaneously obtain information such as azimuth, elevation angle, distance, intensity, speed, attitude, and even shape parameters.

[0096] Specifically, the radar sensor can be configured to be used on a computer, or on other terminals or working devices. The radar data includes coordinate data representing the position, and also includes the timestamp corresponding to the obtained coordinate data. Among them, the time comes from the device on which the radar sensor is configured. The GNSS / RTK sensor needs to be configured on the same device as the radar sensor, or on different devices that have established communication. The GNSS / RTK data includes coordinate data representing the position, and also includes the timestamp corresponding to the obtained coordinate data. Among them, the time comes from the device on which the GNSS / RTK sensor is configured.

[0097] S102. Perform time synchronization processing on the radar data and the GNSS / RTK data to obtain the corresponding radar data and GNSS / RTK data at different times.

[0098] Specifically, the acquisition frequency of the radar data is different from that of the GNSS / RTK data. At a certain moment, there may be only one set of radar data, or only one set of GNSS / RTK data. Taking a moving vehicle as an example, as time changes, the position of the vehicle also moves. Therefore, the position information in the radar data obtained at time t1 and the position information in the GNSS / RTK data obtained at time t2 are not comparable. Therefore, it is necessary to screen out the valid data in the radar data and the GNSS / RTK data, that is, the data with similar acquisition times in the two types of data. The specific method of screening data is that when the time difference between time t1 and time t2 is less than a predetermined threshold, it is considered that the time is aligned. When the time is aligned, it can be considered that the radar data and the GNSS / RTK data obtained at the same moment are comparable in terms of the information representing the position.

[0099] S103. Set the initial radar weight factor and GNSS / RTK weight factor respectively according to the matching degree of the point clouds and the signal strength of the GNSS / RTK data at adjacent times.

[0100] Among them, the point clouds at adjacent times refer to two adjacent frames of point clouds. The point cloud is established based on the radar data. The radar detects a certain space according to the angle, and the points at all angles are combined to form a frame of point cloud. Specifically, the matching of two adjacent frames of point clouds includes four steps: extracting key points and performing feature description, matching the feature points of the two point clouds, removing some mis-matched feature points and performing rough matching, and precise matching. Among them, rough matching can use the clustering algorithm, and precise matching can use the ICP algorithm. In the actual application process, appropriate methods can be selected for point cloud matching according to the accuracy requirements and technical conditions.

[0101] Step S103 includes two aspects. One is to determine the initial radar weight factor GB Lidar, and secondly, determine the initial GNSS / RTK weight factor GA GNSS / RTK The above two initial weight factors are both measures of the credibility of their own data.

[0102] For example, GB Lidar According to the matching degree of two adjacent frames of point cloud, it is assumed that the first set of point cloud P = {p1, p2, p3...p n}, the camera obtains the second set of point clouds Q = {q1, q2, q3...q n}, where the number of points in P and Q is the same. The coordinate origins of the two sets of point clouds are both the optical center of the camera, and P and Q are two different coordinate systems before and after the movement. The point cloud storage order is screened and adjusted by the relevant algorithm, so that the points in P and Q correspond one to one, that is, p n ,q n is the same point in three-dimensional space.

[0103] Furthermore, assuming that the camera position transformation process is: rotation R and translation t, ideally, q n =Rp n +t. However, due to noise or errors such as mismatching (for example, p3 and q3 are not the same point in space, but the algorithm mistakenly considers them to be the same point), the ideal situation is broken, and another minimization objective function needs to be set for matching calculation.

[0104] The above errors reflect the reliability of radar data. Therefore, according to the matching degree of point clouds of two adjacent frames, the initial radar weight factor GB is set. Lidar , which is a measure of the credibility of the radar data itself.

[0105] For example, GA GNSS / RTK The signal strength of the GNSS / RTK data is used to determine the reliability of the data. It is easy to understand that the obstruction of buildings, trees and other obstacles, electromagnetic interference during signal transmission, etc. will interfere with the signals transmitted by GNSS navigation satellites. Therefore, the initial GNSS / RTK weight factor GA is determined based on the signal strength of the GNSS / RTK data. GNSS / RTK , which is a measure of the credibility of the GNSS / RTK data itself.

[0106] S104. Based on the coordinates and distribution information of the point cloud, obtain the degree of openness around the current position, adjust the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness around the current position, and obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor; wherein the point cloud is established based on the radar data.

[0107] Specifically, by performing semantic calculation and analysis on point cloud data, the coordinates and distribution (height, distance, orientation) of the point cloud can be obtained. Among them, point cloud semantics can be divided into a shape set and a structure set. The shape set includes elements such as 2D lines (e.g., 2D contours, straight lines, and curves), object surfaces (e.g., planes, curved surfaces), three-dimensional bodies (e.g., cubes and cylinders, etc.), and 3D boundaries. The structure set includes elements such as normal vectors, curvatures, and convexities. For example, utility poles in the point cloud can be detected using line semantics.

[0108] Furthermore, based on the coordinates and distribution of the point cloud, the degree of openness of the area can be analyzed. As described above, when lidar works, it requires obstacles to reflect light. Therefore, compared with open areas, it is more suitable for areas with buildings. When GNSS / RTK receives navigation satellite signals, obstacles should be avoided to cause interference. Therefore, compared with areas with dense buildings, it is more suitable for open and unobstructed areas. Therefore, according to the degree of openness, the initial lidar weight factor and the initial GNSS / RTK weight factor can be adjusted, and data with high credibility can be selected for mapping.

[0109] The two technologies complement each other. For example, when the lidar data credibility is low in open areas, the GNSS / RTK technology can ensure the accuracy of the global map; for example, when the GNSS / RTK data credibility is low in areas with dense buildings, the lidar technology can ensure the accuracy of the global map.

[0110] For example, in open and textureless areas, reduce GB Lidar ; in open places, increase GA GNSS / RTK ; in areas with dense buildings, increase GB Lidar and at the same time reduce GA GNSS / RTK Among them, open means that there are no objects around the lidar. For example, the mobile platform is in a large open space. At this time, except for the ground, no other objects can be observed in the point cloud; lack of texture means that the observed object has few features. For example, a very large and flat wall. For such an observation target, when the lidar moves in a direction parallel to the wall, there will be no change in point cloud matching, resulting in an increase in positioning error. Therefore, in open and textureless areas, the lidar weight factor is reduced.

[0111] S105. Perform adjustment processing again according to the adjusted lidar weight factor and the adjusted GNSS / RTK weight factor to obtain the final lidar weight factor and GNSS / RTK weight factor; among them, the sum of the final lidar weight factor and the GNSS / RTK weight factor is 1.

[0112] The process of step S105 is normalization processing. Since only two weight factors are adopted in this embodiment and they are in a complementary relationship where one increases while the other decreases, normalization processing is adopted. This is also beneficial for using the mapping algorithm to create a map.

[0113] S106. According to the final radar weight factor and GNSS / RTK weight factor, use the mapping algorithm to establish a map. That is, an optimized map integrating lidar data and GNSS / RTK data is obtained.

[0114] This application collects two types of data, radar and GNSS / RTK. By setting weight factors and based on point cloud semantic analysis to adjust the weight factors, joint mapping of radar technology and GNSS / RTK technology is achieved, ensuring the accuracy of the global map during large-scale mapping.

[0115] Furthermore, the preprocessing of radar data and GNSS / RTK data before S103 also includes: performing coordinate transformation processing on the radar data to obtain radar data in the geographic coordinate system. The above coordinate transformation processing can be located before the time synchronization processing in step S102. To reduce the computational amount, the coordinate transformation processing can be located after the time synchronization processing in step S102.

[0116] It is easy to understand that unifying the coordinate system is beneficial for the fusion of radar data and GNSS / RTK data. Since radar data is data with its own sensor as the origin coordinate and GNSS / RTK data is in the geographic coordinate system, it is beneficial to directly use radar data for mapping and positioning by converting the radar data to the geographic coordinate system. An optional coordinate transformation method is: pre-calibrate the external parameters of the radar sensor and GNSS / RTK sensor (i.e., the distance parameters between the two sensors), and based on the external parameters, convert the radar data to the geographic coordinate system, with the north direction in the geographic coordinate system as the positive x-axis direction and the east direction as the positive y-axis direction.

[0117] In one example, in step S103, according to the matching degree of point clouds at adjacent times, set the initial radar weight factor, which specifically includes:

[0118] S201. Based on the radar data at adjacent times, obtain the point clouds at adjacent times;

[0119] S202. According to the point clouds at adjacent times, calculate and obtain the initial radar weight factor based on the first formula; where the first formula includes:

[0120]

[0121] where GB Lidar is the initial radar weight factor; P match is the number of points that match successfully between the point clouds at adjacent times, Psum is the number of points in the point cloud at the later moment among adjacent moments, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

[0122] The first formula provides a feasible method for obtaining the initial radar weight factor. According to the matching degree of two adjacent frames of point clouds, the radar weight factor is determined to characterize the credibility of radar data.

[0123] In one example, in step S103, according to the signal strength of GNSS / RTK data, an initial GNSS / RTK weight factor is set, which specifically includes:

[0124] According to the signal strength of GNSS / RTK data, based on the second formula, the initial GNSS / RTK weight factor is calculated; where the second formula includes:

[0125] GA GNSS / RTK = k × (∑(P i - P A ) / ∑(P j - P A ))

[0126] Where, GA GNSS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th star that captures the signal strength that meets the requirements; P A is the signal strength required to capture satellites; P j is the signal strength when all satellite signals are theoretically the strongest; k is a predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

[0127] The above second formula provides a feasible method for obtaining the initial GNSS / RTK weight factor. According to the GNSS / RTK data signal strength, the GNSS / RTK weight factor is determined to characterize the credibility of GNSS / RTK data.

[0128] Next, according to the working mode of the GNSS / RTK sensor, the second formula is further introduced. In different working modes, the value of the coefficient k is different, and the positioning accuracies of different positioning modes vary greatly. Therefore, the weight coefficients α, β, γ, δ are also different. The second formula is specifically as follows:

[0129]

[0130] When the number of satellites that capture the signal strength that meets the requirements is less than the requirement, GA GNSS / RTKIt is denoted as 0. Among them, in the actual application process, the number of satellites required to meet the signal strength can be set according to the requirement for accuracy. Satellite positioning is achieved by using observation values such as the pseudorange, ephemeris, and satellite transmission time of a group of satellites, and the user clock error must also be known. Therefore, to obtain the three-dimensional coordinates of a ground point, it is necessary to measure 4 satellites.

[0131] The GNSS mode is single-point positioning, which determines the position of the observation station relative to the earth's centroid in the earth protocol coordinate system. During this positioning process, there are three parts of errors. One part is common to each user receiver, such as satellite clock error, ephemeris error, ionospheric error, tropospheric error, etc.; the second part is the propagation delay error that cannot be measured by the user or calculated by the correction model; the third part is the error inherent in each user receiver, such as internal noise, channel delay, multipath effect, etc. By using differential technology, the first part of the error can be completely eliminated, the second part of the error can be mostly eliminated, which mainly depends on the distance between the reference receiver and the user receiver, and the third part of the error cannot be eliminated.

[0132] To reduce the error, the differential positioning method is proposed, that is, using a GPS reference receiver (reference station) and a user receiver (mobile station), and using real-time or post-processing technology to eliminate the common error sources during user measurement - satellite orbit error, satellite clock error, atmospheric delay, and multipath effect. The working principle of differential positioning technology is the same, that is, the reference station sends correction data, and the user station receives and corrects its measurement results to obtain an accurate positioning result. The difference is that the specific content of the sent correction data is different, and its differential positioning accuracy is also different.

[0133] Pseudorange differential positioning (differential global navigation satellite system, DGNSS) mode. The receiver at the reference station calculates its distance to the visible satellites and compares this calculated distance with the measured value containing errors. Use an α-β filter to filter this difference and find its deviation. Then transmit the ranging errors of all satellites to the user, and the user uses this ranging error to correct the measured pseudorange. Finally, the user uses the corrected pseudorange to solve its own position, and the common error can be eliminated to improve the positioning accuracy. Similar to position difference, pseudorange difference can cancel the common error between the two stations, but as the distance between the user and the reference station increases, a systematic error appears again, and this error cannot be eliminated by any differential method. The distance between the user and the reference station has a decisive impact on the accuracy.

[0134] RTK (real time kinematic, carrier phase difference) mode. RTK technology is based on timely processing of the carrier phase of two measuring stations. Carrier phase difference technology can provide the three-dimensional coordinates of the observation point in real time and achieve high accuracy at the centimeter level. Similar to the principle of pseudo-range difference, the base station transmits its carrier observation value and base station coordinate information to the user station through the data link in a timely manner. The user station receives the carrier phase of the positioning satellite and the carrier phase from the base station, and forms a phase difference observation value for timely processing, which can provide centimeter-level positioning results in a timely manner.

[0135] The carrier phase differential mode is divided into RTK fixed solution (fix) mode and RTK floating point solution (float) mode. Having a fixed solution means that the correct solution has been calculated. Under normal conditions, a measurement accuracy of 1 to 3 cm can be achieved. The RTK floating point solution is also called a differential solution, and the algorithm has not yet obtained a fixed solution. Since there is no fixed solution, a floating point solution is provided, and its position is always less accurate than the fixed solution, with an accuracy between centimeters and meters.

[0136] In addition, it should be noted that the summation symbol in the second formula represents the sum of the signal strength related data of each satellite. In different working modes, the number of captured satellites is also different.

[0137] In one example, in step S104, based on the coordinates and distribution information of the point cloud, the degree of openness around the current position is obtained, and the initial radar weight factor and the initial GNSS / RTK weight factor are adjusted according to the degree of openness to obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, which specifically includes:

[0138] According to the coordinates and distribution information of the point cloud, based on the third formula, the adjusted GNSS / RTK weight factor is calculated; wherein the third formula includes:

[0139]

[0140] Among them, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the point cloud p i The cosine value of the angle between the point and the vertical upward direction of the ground;

[0141] According to the coordinates and distribution information of the point cloud, based on the fourth formula, the adjusted radar weight factor is calculated; wherein the fourth formula includes:

[0142]

[0143] Among them, GC Lidar is the adjusted radar weight factor; is the number of points belonging to the plane in the point cloud; is the number of points belonging to the edge in the point cloud; is the number of points belonging to the corner in the point cloud; Sum P is the total number of points in the point cloud; a, b, and c are the weight coefficients of the plane feature, edge feature, and corner feature respectively.

[0144] In the actual point cloud matching process, the corner feature is the most obvious and has the largest weight, while the plane feature has the smallest significance and the smallest weight. Therefore, c > b > a.

[0145] The above third formula and fourth formula provide a feasible method for adjusting the weight factor. The radar weight factor and GNSS / RTK weight factor are adjusted according to the emptiness degree of the area, effectively integrating the radar technology and GNSS / RTK technology. At the same time, according to the respective practical conditions of the radar technology and GNSS / RTK technology, their data contribution degrees in the mapping process are reasonably allocated to ensure the accuracy and consistency of the global map.

[0146] In one example, step S106 specifically includes:

[0147] Based on the fifth formula, the final radar weight factor and GNSS / RTK weight factor are obtained; among them, the fifth formula includes:

[0148]

[0149]

[0150] Among them, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

[0151] The above processing is normalization processing. Since only two weight factors are adopted in this embodiment and they have a complementary relationship of mutual increase and decrease, normalization processing is adopted. It is also beneficial for using the mapping algorithm to draw the map.

[0152] Furthermore, the aforementioned mapping and positioning method further includes: obtaining the distance between the moving object and the building.

[0153] Based on the distance between the moving object and the building, step S103 specifically includes:

[0154] S301. If the distance is less than a preset first threshold, set an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; wherein, the initial GNSS / RTK weight factor is not greater than the preset first threshold.

[0155] S302. If the distance exceeds a preset second threshold, set an initial radar weight factor according to the matching degree of the valid radar data at adjacent moments; wherein, the initial radar weight factor is not greater than the preset second threshold.

[0156] Specifically, in the process of joint mapping and positioning of the radar and GNSS / RTK, by analyzing the changes in the radar weight factor and the GNSS / RTK weight factor, reasonably plan the path to make GC GNSS / RTK and GC Lidar increase as much as possible to achieve mapping and positioning in the best state of the sensor. The above settings avoid being too close or too far from the building, ensure that the values of the radar weight factor and the GNSS / RTK weight factor are comparable, and enable both the radar mapping and positioning technology and the GNSS / RTK mapping and positioning technology to play a certain role.

[0157] Figure 3 FIG. 14 is a flowchart of another mapping and positioning method provided in the first embodiment of the present application. Taking a 3D lidar as an example, first obtain 3D lidar data and GNSS / RTK data; then perform data preprocessing, including time synchronization processing and coordinate transformation processing; further, process the GNSS / RTK data to analyze the signal strength of the GNSS / RTK data, and then set the GNSS / RTK weight factor; point cloud matching is used to set the radar weight factor; point cloud semantic analysis is used to adjust the GNSS / RTK weight factor and the radar weight factor; finally, calculate the mapping in the Graph algorithm based on the final GNSS / RTK weight factor and radar weight factor. During the joint mapping process, based on the optimized map, reasonably plan the navigation path; at the same time, the optimization of the navigation path realizes mapping and positioning in the best state of the sensor.

[0158] By collecting 3D lidar data and GNSS / RTK data as described above, calculating the weight factors of the radar and GNSS / RTK according to point cloud semantic analysis; using the 3D lidar technology and the GNSS / RTK technology for joint mapping and positioning, a point cloud map aligned with the geographical coordinate system can be established. Based on the Graph algorithm optimization of the weight factors by point cloud semantics, the radar and GNSS / RTK data can be effectively fused, and the global Figure 1 consistency is good; during the process of joint mapping and positioning of the 3D lidar and GNSS / RTK, by analyzing the changes in the weight factors of the radar and GNSS / RTK, reasonably plan the path to realize mapping and positioning in the best state of the sensor.

[0159] In the actual application process, an alternative solution is to use devices such as 4D millimeter-wave radars to replace lidars. Semantic analysis is performed on the obtained dense point cloud, and the Graph weighting factor is calculated by fusing with GNSS / RTK data to achieve mapping and positioning. Another alternative solution is to use binocular vision to replace lidars. Semantic analysis is performed on the images, and the Graph weighting factor is calculated by fusing with GNSS / RTK data to establish a sparse / dense point cloud map and achieve mapping and positioning.

[0160] The present application provides a mapping and positioning method, including performing time synchronization processing and coordinate transformation processing on the radar data collected by the radar sensor and the GNSS / RTK data collected by the GNSS / RTK sensor; respectively setting initial radar weighting factors and GNSS / RTK weighting factors according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data at adjacent moments; adjusting the initial radar weighting factors and GNSS / RTK weighting factors based on the coordinates and distribution information of the point cloud, and then performing normalization processing to obtain the final radar weighting factors and GNSS / RTK weighting factors; using a mapping algorithm to establish a map according to the final radar weighting factors and GNSS / RTK weighting factors. By setting the radar weighting factors and GNSS / RTK weighting factors, and adjusting the two weighting factors based on the coordinates and distribution information of the point cloud, the present application realizes the effective fusion of radar data and GNSS / RTK data, realizes the joint mapping of radar technology and GNSS / RTK technology, and ensures the accuracy of the global map.

[0161] Embodiment 2

[0162] Figure 4 It is a schematic structural diagram of a mapping and positioning device provided in Embodiment 2 of the present application, including:

[0163] An acquisition module 10, configured to acquire radar data collected by a radar sensor and GNSS / RTK data collected by a GNSS / RTK sensor;

[0164] A processing module 20, configured to perform time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different moments;

[0165] The processing module 20 is further used to set an initial radar weight factor and a GNSS / RTK weight factor according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data at adjacent moments, respectively; and, based on the coordinates and distribution information of the point cloud, obtain the degree of openness around the current position, and adjust the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness around the current position to obtain an adjusted radar weight factor and an adjusted GNSS / RTK weight factor; wherein the point cloud is established based on the radar data;

[0166] The processing module 20 is further used to perform adjustment processing again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain a final radar weight factor and a final GNSS / RTK weight factor; wherein the sum of the final radar weight factor and the GNSS / RTK weight factor is 1;

[0167] The mapping module 30 is used to build a map using a mapping algorithm according to the final radar weight factor and the GNSS / RTK weight factor.

[0168] Specifically, the mapping and positioning device first collects radar and GNSS / RTK data; then it performs time synchronization processing on the two data to obtain time-aligned valid data. Next, it sets two weight factors respectively to evaluate its own credibility. Then, based on the semantic analysis of the point cloud, it adjusts the two weight factors to achieve effective fusion of radar data and GNSS / RTK data; finally, based on the final radar weight factor and GNSS / RTK weight factor, it uses the mapping algorithm to build a map, realizing the joint mapping of radar technology and GNSS / RTK technology, making the global map more accurate. Figure 1 Good consistency and high accuracy.

[0169] Furthermore, the processing module 20 is used to perform coordinate conversion processing on the radar data before setting the initial radar weight factor and GNSS / RTK weight factor according to the matching degree of the point clouds at adjacent time moments and the signal strength of the GNSS / RTK data at different time moments, so as to obtain the radar data in the geographic coordinate system.

[0170] Specifically, a unified coordinate system is conducive to the integration of radar data and GNSS / RTK data. Since radar data is data with its own sensor as the origin coordinate, and GNSS / RTK data is a geographic coordinate system, choosing to convert radar data to a geographic coordinate system is conducive to directly using radar data for mapping and positioning. An optional coordinate conversion method is to calibrate the external parameters of the radar sensor and GNSS / RTK sensor (i.e., the distance parameters of the two sensors) in advance, and based on the external parameters, convert the radar data to a geographic coordinate system, where the north is the positive direction of the x-axis and the east is the positive direction of the y-axis.

[0171] In one example, the processing module 20 is configured to set an initial radar weight factor according to the matching degree of point clouds at adjacent moments, specifically including:

[0172] Obtain point clouds at adjacent moments based on radar data at adjacent moments;

[0173] Calculate and obtain an initial radar weight factor based on the point clouds at adjacent moments according to the first formula; wherein, the first formula includes:

[0174]

[0175] wherein, GB Lidar is the initial radar weight factor; P match is the number of successfully matched points between point clouds at adjacent moments, P sum is the number of points in the point cloud at the latter moment in adjacent moments, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

[0176] The first formula provides a feasible method for obtaining the initial radar weight factor. According to the matching degree of two adjacent frames of point clouds, the radar weight factor is determined to represent the credibility of radar data.

[0177] In one example, the processing module 20 is configured to set an initial GNSS / RTK weight factor according to the signal strength of GNSS / RTK data, specifically including:

[0178] Calculate and obtain an initial GNSS / RTK weight factor based on the signal strength of GNSS / RTK data according to the second formula; wherein, the second formula includes:

[0179] GA GNsS / RTK = k × (∑(P i - P A ) / ∑(P j - P A ))

[0180] wherein, GA GNSS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th star captured to meet the signal strength; P A is the signal strength required to capture a satellite; P j is the signal strength when all satellite signals are theoretically the strongest; k is a predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

[0181] The second formula above provides a feasible method for obtaining the initial GNSS / RTK weight factor. According to the GNSS / RTK data signal strength, the GNSS / RTK weight factor is determined to characterize the credibility of the GNSS / RTK data. The summation symbol in brackets in the second formula represents the sum of the signal strength related data of each satellite. In different working modes, the number of captured satellites is also different.

[0182] The second formula is further introduced below according to the working mode of the GNSS / RTK sensor. In different working modes, the value of coefficient k is different. The second formula is as follows:

[0183]

[0184] GNSS satellite positioning is achieved by using a set of satellite pseudoranges, ephemeris, satellite launch time and other observation values, and the user clock error must also be known. Therefore, to obtain the three-dimensional coordinates of the ground point, four satellites must be measured. In this positioning process, there are three parts of errors. One part is common to every user receiver, such as satellite clock error, ephemeris error, ionosphere error, troposphere error, etc.; the second part is the propagation delay error that cannot be measured by the user or calculated by the correction model; the third part is the error inherent in each user receiver, such as internal noise, channel delay, multipath effect, etc. Using differential technology, the first part of the error can be completely eliminated, and the second part of the error can be mostly eliminated, which mainly depends on the distance between the reference receiver and the user receiver. The third part of the error cannot be eliminated.

[0185] In order to reduce errors, a differential positioning method was proposed, which uses a GPS reference receiver (reference station) and a user receiver (mobile station), and utilizes real-time or post-processing technology to eliminate common error sources during user measurements - satellite orbit error, satellite clock error, atmospheric delay, and multipath effect.

[0186] Differential positioning technology can be divided into three categories according to the information sent by the base station of differential positioning: position difference, pseudo-range difference and carrier phase difference. The working principles of these three types of differential methods are the same, that is, the base station sends correction data, and the user station receives and corrects its measurement results to obtain accurate positioning results. The difference is that the specific content of the correction data sent is different, and the differential positioning accuracy is also different.

[0187] In one example, the processing module 20 is used to obtain the degree of openness around the current position based on the coordinates and distribution information of the point cloud, adjust the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness, and obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, specifically including:

[0188] Based on the coordinate and distribution information of the point cloud, and based on the third formula, calculate the adjusted GNSS / RTK weight factor; wherein, the third formula includes:

[0189]

[0190] wherein, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the cosine value of the angle between the p i point in the point cloud and the direction perpendicular to the ground upwards;

[0191] Based on the coordinate and distribution information of the point cloud, and based on the fourth formula, calculate the adjusted radar weight factor; wherein, the fourth formula includes:

[0192]

[0193] wherein, GC Lidar is the adjusted radar weight factor; is the number of points belonging to the plane in the point cloud; is the number of points belonging to the edge in the point cloud; is the number of points belonging to the corner in the point cloud; Sum P is the total number of points in the point cloud; a, b, and c are the weight coefficients of the plane feature, edge feature, and corner feature respectively.

[0194] In the actual point cloud matching process, the corner feature is the most obvious and has the largest weight, while the plane feature has the smallest significance and the smallest weight. Therefore, c > b > a.

[0195] The above third formula and fourth formula provide a feasible method for adjusting the weight factor. Adjust the radar weight factor and GNSS / RTK weight factor according to the openness of the area, effectively integrating the radar technology and GNSS / RTK technology. At the same time, according to the respective practical conditions of the radar technology and GNSS / RTK technology, reasonably allocate their data contribution degrees in the mapping process to ensure the accuracy and consistency of the global map.

[0196] In one example, the processing module 20 is used to perform adjustment processing again based on the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain the final radar weight factor and GNSS / RTK weight factor, specifically including:

[0197] Based on the fifth formula, obtain the final radar weight factor and GNSS / RTK weight factor; wherein, the fifth formula includes:

[0198]

[0199]

[0200] Among them, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

[0201] The above processing is normalization processing. Since only two weight factors are adopted in this embodiment and they are in a complementary relationship where one increases while the other decreases, normalization processing is adopted. It is also beneficial for using the mapping algorithm to draw maps.

[0202] In one example, the mapping and positioning device further includes: a navigation module 40, as Figure 5 shown. Figure 5 This is a schematic structural diagram of another mapping and positioning device provided in the second embodiment of the present application. Among them, the navigation module is used to obtain the distance between the moving object and the building.

[0203] Further, if the distance is less than a preset first threshold, the processing module 20 sets an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; among them, the initial GNSS / RTK weight factor is not greater than the preset first threshold. If the distance exceeds a preset second threshold, the processing module 20 sets an initial radar weight factor according to the matching degree of the point cloud at adjacent moments; among them, the initial radar weight factor is not greater than the preset second threshold.

[0204] Specifically, during the combined mapping and positioning process of the radar and GNSS / RTK, by analyzing the changes in the radar weight factor and the GNSS / RTK weight factor, the path is reasonably planned to make GC GNSS / RTK and GC Lidar increase as much as possible, so as to realize mapping and positioning in the best state of the sensor. The above settings avoid being too close or too far from the building, ensure that the values of the radar weight factor and the GNSS / RTK weight factor are comparable, and enable both the radar mapping and positioning technology and the GNSS / RTK mapping and positioning technology to play a certain role.

[0205] The present application provides a mapping and positioning device, including an acquisition module 10, which is used to acquire radar data collected by a radar sensor and GNSS / RTK data collected by a GNSS / RTK sensor; a processing module 20, which is used to perform time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different times; the processing module 20 is also used to set an initial radar weight factor and a GNSS / RTK weight factor according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data at adjacent times respectively; and, based on the coordinates and distribution information of the point cloud, obtain the degree of openness around the current position, and calculate the distance around the current position according to the matching degree of the point cloud and the signal strength of the GNSS / RTK data. The initial radar weight factor and the initial GNSS / RTK weight factor are adjusted according to the degree of emptiness of the edge to obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor; wherein, the point cloud is established based on the radar data; the processing module 20 is also used to adjust the processing again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain the final radar weight factor and GNSS / RTK weight factor; wherein, the sum of the final radar weight factor and the GNSS / RTK weight factor is 1; the mapping module 30 is used to establish a map using a mapping algorithm based on the final radar weight factor and the GNSS / RTK weight factor. This application realizes the effective fusion of radar data and GNSS / RTK data by setting the radar weight factor and the GNSS / RTK weight factor, and adjusting the two weight factors based on the coordinates and distribution information of the point cloud, thereby realizing the joint mapping of radar technology and GNSS / RTK technology and ensuring the accuracy of the global map.

[0206] Embodiment 3

[0207] Figure 6 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present application is shown in FIG. Figure 6 As shown, the electronic equipment includes:

[0208] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, the memory 292, and the communication interface 293 may communicate with each other through the bus 294. The communication interface 293 may be used for information transmission. The processor 291 may call the logic instructions in the memory 292 to execute the method of the above embodiment.

[0209] In addition, the logic instructions in the above-mentioned memory 292 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0210] The memory 292 serves as a computer-readable storage medium and can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of the present application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, implements the methods in the above method embodiments.

[0211] The memory 292 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0212] The present application provides a computer-readable storage medium, in which computer-execution instructions are stored, and when the computer-execution instructions are executed by a processor, they are used to implement the method provided in Embodiment 1.

[0213] Those skilled in the art will readily think of other implementation schemes of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0214] It should be understood that the present application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A mapping and positioning method, characterized in that, include: Acquire radar data collected by radar sensors, and GNSS / RTK data collected by global navigation satellite system GNSS or real-time carrier phase differential RTK sensors; Performing time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different times; The initial radar weight factor and the GNSS / RTK weight factor are set according to the matching degree of the point clouds at adjacent moments and the signal strength of the GNSS / RTK data respectively; and, based on the coordinates and distribution information of the point cloud, obtaining the degree of openness around the current position, adjusting the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness around the current position, and obtaining the adjusted radar weight factor and the adjusted GNSS / RTK weight factor; wherein the point cloud is established based on the radar data; According to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, an adjustment process is performed again to obtain a final radar weight factor and a final GNSS / RTK weight factor; wherein the sum of the final radar weight factor and the GNSS / RTK weight factor is 1; Based on the final radar weight factor and GNSS / RTK weight factor, a map is created using a mapping algorithm.

2. The method according to claim 1, wherein Before setting the initial radar weight factor and the GNSS / RTK weight factor according to the matching degree of the point clouds at adjacent moments and the signal strength of the GNSS / RTK data at different moments, the method further includes: Coordinate conversion is performed on the radar data to obtain radar data in a geographic coordinate system.

3. The method according to claim 1, characterized in that The initial radar weight factor is set according to the matching degree of the point clouds at adjacent moments, including: Based on the radar data at adjacent moments, point clouds at adjacent moments are obtained; According to the point clouds at the adjacent moments, an initial radar weight factor is calculated based on a first formula; wherein the first formula includes: Among them, GB Lidar is the initial radar weight factor; P match is the number of points successfully matched between point clouds at adjacent times, and P sum is the number of points in the point cloud at the latter time in the adjacent times, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

4. The method according to claim 1, characterized in that, The step of setting an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data comprises: According to the signal strength of the GNSS / RTK data, an initial GNSS / RTK weight factor is calculated based on a second formula; wherein the second formula includes: Among them, GA GNSS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th star captured to meet the signal strength; P A is the signal strength required to capture the satellite; P j is the signal strength when all satellite signals are theoretically the strongest; k is a predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

5. The method according to claim 1, wherein The method of obtaining the degree of openness around the current position based on the coordinates and distribution information of the point cloud, adjusting the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness, and obtaining the adjusted radar weight factor and the adjusted GNSS / RTK weight factor includes: According to the coordinates and distribution information of the point cloud, based on the third formula, the adjusted GNSS / RTK weight factor is calculated; wherein the third formula includes: Among them, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the cosine value of the angle between the line connecting the p i point in the point cloud to the radar sensor and the direction vertically upward from the ground; According to the coordinates and distribution information of the point cloud, based on the fourth formula, the adjusted radar weight factor is calculated; wherein the fourth formula includes: Among them, GC Lidar is the adjusted radar weight factor; is the number of points belonging to the plane in the point cloud; is the number of points belonging to the edge in the point cloud; is the number of points belonging to the corner in the point cloud; Sum P is the total number of points in the point cloud; a, b, and c are the weight coefficients of the plane feature, edge feature, and corner feature, respectively.

6. The method according to claim 1, wherein The step of performing adjustment again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain the final radar weight factor and the GNSS / RTK weight factor includes: Based on the fifth formula, the final radar weight factor and GNSS / RTK weight factor are obtained; wherein the fifth formula includes: Among them, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

7. The method according to claim 1, wherein The method further comprises: Get the distance between the moving object and the building; The initial GNSS / RTK weight factor is set according to the signal strength of the GNSS / RTK data, specifically including: If the distance is less than a preset first threshold, setting an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; wherein the initial GNSS / RTK weight factor is not greater than the preset first threshold; The initial radar weight factor is set according to the matching degree of the point clouds at adjacent moments, specifically including: If the distance exceeds a preset second threshold, an initial radar weight factor is set according to the matching degree of the point clouds at adjacent moments; wherein the initial radar weight factor is not greater than the preset second threshold.

8. A mapping and positioning device, characterized in that, include: An acquisition module is used to acquire radar data collected by a radar sensor and GNSS / RTK data collected by a GNSS / RTK sensor; A processing module, used for performing time synchronization processing on the radar data and the GNSS / RTK data to obtain corresponding radar data and GNSS / RTK data at different times; The processing module is further used to set an initial radar weight factor and a GNSS / RTK weight factor according to the matching degree of the radar data and the signal strength of the GNSS / RTK data at adjacent moments respectively; and, based on the coordinates and distribution information of the point cloud, obtaining the degree of openness around the current position, adjusting the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness around the current position, and obtaining the adjusted radar weight factor and the adjusted GNSS / RTK weight factor; wherein the point cloud is established based on the radar data; The processing module is further used to perform adjustment processing again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain a final radar weight factor and a final GNSS / RTK weight factor; wherein the sum of the final radar weight factor and the GNSS / RTK weight factor is 1; The mapping module is used to build a map using a mapping algorithm based on the final radar weight factor and GNSS / RTK weight factor.

9. The device according to claim 8, wherein The processing module is used to perform coordinate conversion processing on the radar data before setting the initial radar weight factor and GNSS / RTK weight factor according to the matching degree of point clouds at adjacent moments and the signal strength of GNSS / RTK data at different moments, so as to obtain radar data in a geographic coordinate system.

10. The device according to claim 8, wherein The processing module is used to set the initial radar weight factor according to the matching degree of the point clouds at adjacent moments, specifically including: Based on the radar data at adjacent moments, point clouds at adjacent moments are obtained; According to the point clouds at the adjacent moments, an initial radar weight factor is calculated based on a first formula; wherein the first formula includes: Among them, GB Lidar is the initial radar weight factor; P match is the number of points successfully matched between point clouds at adjacent times, and P sum is the number of points in the point cloud at the later time among the adjacent times, and ε is a coefficient obtained according to the measurement accuracy of the radar sensor.

11. The device according to claim 8, wherein The processing module is used to set an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data, specifically including: According to the signal strength of the GNSS / RTK data, an initial GNSS / RTK weight factor is calculated based on a second formula; wherein the second formula includes: Among them, GA GNsS / RTK is the initial GNSS / RTK weight factor; P i is the signal strength of the i-th star captured to meet the signal strength; P A is the signal strength required to capture a satellite; P j is the signal strength when all satellite signals are theoretically the strongest; k is a predetermined coefficient corresponding to different working modes of the GNSS / RTK sensor.

12. The device according to claim 8, wherein The processing module is used to obtain the degree of openness around the current position based on the coordinates and distribution information of the point cloud, and adjust the initial radar weight factor and the initial GNSS / RTK weight factor according to the degree of openness to obtain the adjusted radar weight factor and the adjusted GNSS / RTK weight factor, specifically including: According to the coordinates and distribution information of the point cloud, based on the third formula, the adjusted GNSS / RTK weight factor is calculated; wherein the third formula includes: Among them, GC GNSS / RTK is the adjusted GNSS / RTK weight factor; is the number of all points in the point cloud; cos(p i ) is the cosine value of the angle between the line connecting the p i point in the point cloud to the radar sensor and the direction perpendicular to the ground upward; According to the coordinates and distribution information of the point cloud, based on the fourth formula, the adjusted radar weight factor is calculated; wherein the fourth formula includes: Among them, GC Lidar is the adjusted radar weight factor; is the number of points belonging to the plane in the point cloud; is the number of points belonging to the edge in the point cloud; is the number of points belonging to the corner in the point cloud; Sum p is the total number of points in the point cloud; a, b, and c correspond to the weight coefficients of the plane feature, edge feature, and corner feature respectively.

13. The device according to claim 8, characterized in that, The processing module is used to perform adjustment processing again according to the adjusted radar weight factor and the adjusted GNSS / RTK weight factor to obtain the final radar weight factor and the GNSS / RTK weight factor, specifically including: Based on the fifth formula, the final radar weight factor and GNSS / RTK weight factor are obtained; wherein the fifth formula includes: Among them, GC’ GNSS / RTK is the final GNSS / RTK weight factor; GC’ Lidar is the final radar weight factor; GC GNSS / RTK is the adjusted GNSS / RTK weight factor; GC Lidar is the adjusted radar weight factor.

14. The device according to claim 8, wherein The device further comprises: a navigation module; The navigation module is used to obtain the distance between the moving object and the building; The processing module is used to set an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data, specifically including: If the distance is less than a preset first threshold, setting an initial GNSS / RTK weight factor according to the signal strength of the GNSS / RTK data; wherein the initial GNSS / RTK weight factor is not greater than the preset first threshold; The processing module is used to set the initial radar weight factor according to the matching degree of the point clouds at adjacent moments, specifically including: If the distance exceeds a preset second threshold, an initial radar weight factor is set according to the matching degree of the point clouds at adjacent moments; wherein the initial radar weight factor is not greater than the preset second threshold.

15. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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