A positioning and mapping method and system for mobile robots

Abstract

The present invention discloses a positioning and mapping method and system for a mobile robot, comprising: arranging a single-line laser radar on the mobile robot, respectively acquiring first radar data of the first single-line laser radar and second radar data of the second single-line laser radar; based on the relative position data between the single-line laser radars, fusing the first radar data and the second radar data to generate point cloud data; based on the point cloud data, performing point cloud matching, acquiring sub-map data and relative posture data corresponding to the sub-map data, and generating a first positioning map of the mobile robot; the present invention increases the search area, expands the top-level linear search space when using a branch and bound algorithm to solve the optimal match, increases the amount of calculation but ensures the optimality of the solution, improves the mapping and positioning accuracy, increases the environmental information scanning range, and improves the mapping efficiency.

Description

Technical Field

[0001] The present invention relates to the field of mobile robot positioning technology, and in particular to a positioning and mapping method and system for a mobile robot. Background Art

[0002] With the development of modern society, mobile robots are increasingly used in people's daily lives, such as self-driving cars and AGV cars. Mobile robots are widely used in logistics loading and unloading and service industries, with high work efficiency and low labor costs. In the military, mobile robots are also used for military reconnaissance, mine bomb removal, transportation and other military tasks. Mobile robots will become an important industry for future development and an inevitable trend of future industrialization.

[0003] Indoor mobile robots often need to work in unknown indoor environments, which requires high-precision positioning and mapping, namely SLAM. Outdoor positioning systems such as GPS cannot achieve ideal results in indoor environments. Therefore, in an unknown environment, the mobile robot needs to rely on its sensors to perceive the surrounding environment, locate and build an environmental map based on the sensor information. There will be certain errors in positioning and mapping based on a single laser radar, which is not enough to achieve the best results. Therefore, there is an urgent need for a positioning and mapping method and system for mobile robots to improve the environmental adaptability of mobile robots, increase the environmental scanning area, and the positioning and mapping accuracy and efficiency. Summary of the invention

[0004] In order to solve the indoor mapping problem of dual single-line laser radar, the present invention provides a positioning and mapping method for a mobile robot, comprising the following steps:

[0005] The single-line laser radar is arranged on the mobile robot, and first radar data of the first single-line laser radar and second radar data of the second single-line laser radar are respectively obtained;

[0006] Based on the relative position data between the single-line laser radars, the first radar data and the second radar data are fused to generate point cloud data;

[0007] Based on the point cloud data, point cloud matching is performed to obtain sub-map data and relative posture data corresponding to the sub-map data, and generate a first positioning map of the mobile robot.

[0008] Preferably, in the process of generating a positioning map of the mobile robot, the posture data obtained by two single-line laser radar scans are collected and matched with the relative posture data, and the first positioning map is optimized according to the best matching result to generate a second positioning map.

[0009] Preferably, in the process of optimizing the positioning map, the first positioning map is updated according to the second positioning map.

[0010] Preferably, in the process of obtaining the best matching result, based on the branch and bound scanning matching method, the best matching result is generated by collecting posture data and matching it with the relative posture data.

[0011] Preferably, in the process of obtaining the best matching result, the steps of the branch and bound scanning matching method include:

[0012] Obtain the maximum scan data point set of two single-line laser radars, obtain the angle step size according to the number of data points in the maximum scan data point set, and generate the search angle in the map;

[0013] Obtaining a first grid number of the distance between the scanned data point in the x-axis direction and the coordinate origin in the map, and a second grid number of the distance between the scanned data point in the y-axis direction and the coordinate origin in the first positioning map;

[0014] Generate a third grid number in the search space of the map according to the search angle, the first grid number, and the second grid number;

[0015] Based on the number of third grids and the side length of each grid, a search space is generated, and scan matching is performed according to the initial data points used for scanning and the laser scanning data points to obtain the best matching result and the data points corresponding to the best matching result;

[0016] Based on the data points, a second positioning map is generated.

[0017] Preferably, the data point corresponding to the best matching result is expressed as:

[0018]

[0019] Among them, ξ * represents the best matching scan data point, K represents the number of scan data points in the search space, ξ represents the laser scan data point, M nearest Indicates the grid value corresponding to the grid point closest to the new scan data point, T ξ h k represents the position of the LiDAR scan data point in the sub-map coordinate system, and W represents the search space in the actual environment.

[0020] Preferably, in the process of generating the search space, the search space is expressed as:

[0021]

[0022] Among them, ξ 0 represents the initial data point, j x Indicates the number of grids from the scanned data point in the x-axis direction of the map to the coordinate origin of the map; j yIndicates the number of grids from the coordinate origin of the scanned data point in the y-axis direction of the map; j θ Indicates the angle between the scanned data point in the map and the origin of the map coordinates; It represents the number of third grids included in the search space of the map, and r represents the side length of a grid in the grid map.

[0023] Preferably, in the process of generating the third grid quantity, the third grid quantity is expressed as:

[0024]

[0025] in, δ θ represents the angle step, ω x Represents the number of the first grid; ω y , represents the number of the second grid; ω θ is the search angle in the map, W x Indicates the actual search distance in the x-axis direction; W y Indicates the actual search distance in the y-axis direction; W θ Indicates the actual search angle.

[0026] Preferably, in the process of obtaining the angle step, the expression of the angle step is:

[0027]

[0028]

[0029] Among them, d max Indicates the number of data points; h k Represents a set of scanned data points.

[0030] The present invention also discloses a positioning and mapping system based on dual single-line laser radars, comprising:

[0031] A data acquisition module, used for respectively acquiring first radar data of a first single-line laser radar and second radar data of a second single-line laser radar by setting dual single-line laser radars at two ends of the mobile robot, wherein the dual single-line laser radars are used to represent two single-line laser radars set at different ends of the mobile machine;

[0032] A data processing module, used for fusing the first radar data and the second radar data based on the relative position data of the two single-line laser radars to generate point cloud data;

[0033] The map generation module is used to perform point cloud matching based on point cloud data, obtain sub-map data and relative posture data corresponding to the sub-map data, and generate a first positioning map of the mobile robot.

[0034] The present invention discloses the following technical effects:

[0035] The present invention increases the search area, expands the top-level linear search space when using the branch and bound algorithm to solve the optimal match, increases the amount of calculation but ensures the optimality of the solution, and improves the accuracy of mapping and positioning.

[0036] The present invention increases the environmental information scanning range and improves the mapping efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 It is a schematic diagram of the mapping process based on dual single-line laser radar according to the present invention;

[0039] Figure 2 It is a schematic diagram of a mobile robot based on dual single-line laser radars according to the present invention;

[0040] Figure 3 It is a flow chart of the method described in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0042] like Figure 1-3 As shown, the present invention provides a positioning and mapping method for a mobile robot, comprising the following steps:

[0043] The single-line laser radar is arranged on the mobile robot, and first radar data of the first single-line laser radar and second radar data of the second single-line laser radar are respectively obtained;

[0044] Based on the relative position data between the single-line laser radars, the first radar data and the second radar data are fused to generate point cloud data;

[0045] Based on the point cloud data, point cloud matching is performed to obtain sub-map data and relative posture data corresponding to the sub-map data, and generate a first positioning map of the mobile robot.

[0046] In the process of generating a positioning map of a mobile robot, the present invention also includes a map optimization process, which collects posture data obtained by scanning two single-line laser radars, matches them with relative posture data, optimizes the first positioning map according to the best matching result, and generates a second positioning map.

[0047] The process of optimizing the first positioning map of the present invention may also include: updating the first positioning map by using the second positioning map.

[0048] In the process of obtaining the best matching result, the present invention uses a branch and bound scanning matching method to scan and match the positioning map. Specifically, based on the branch and bound scanning matching method, the best matching result is generated by collecting posture data and matching it with the relative posture data.

[0049] In the process of obtaining the best matching result of the present invention, the steps of the branch and bound scanning matching method mentioned include:

[0050] Obtain the maximum scan data point set of two single-line laser radars, obtain the angle step size according to the number of data points in the maximum scan data point set, and generate the search angle in the map;

[0051] Obtaining a first grid number of the distance between the scanned data point in the x-axis direction and the coordinate origin in the map, and a second grid number of the distance between the scanned data point in the y-axis direction and the coordinate origin in the first positioning map;

[0052] Generate a third grid number in the search space of the map according to the search angle, the first grid number, and the second grid number;

[0053] Based on the number of third grids and the side length of each grid, a search space is generated, and scan matching is performed according to the initial data points used for scanning and the laser scanning data points to obtain the best matching result and the data points corresponding to the best matching result;

[0054] Based on the data points, a second positioning map is generated.

[0055] The data points corresponding to the best matching results mentioned in the present invention are expressed as:

[0056]

[0057] Among them, ξ *represents the best matching scan data point, K represents the number of scan data points in the search space, ξ represents the laser scan data point, M nearest Indicates the grid value corresponding to the grid point closest to the new scan data point, T ξ h k represents the position of the LiDAR scan data point in the sub-map coordinate system, and W represents the search space in the actual environment.

[0058] In the process of generating the search space of the present invention, the search space mentioned is expressed as:

[0059]

[0060] Among them, ξ 0 represents the initial data point, j x Indicates the number of grids from the scanned data point in the x-axis direction of the map to the coordinate origin of the map; j y Indicates the number of grids from the coordinate origin of the scanned data point in the y-axis direction of the map; j θ Indicates the angle between the scanned data point in the map and the origin of the map coordinates; It represents the number of third grids included in the search space of the map, and r represents the side length of a grid in the grid map.

[0061] In the process of generating the third grid quantity of the present invention, the third grid quantity mentioned is expressed as:

[0062]

[0063] in, δ θ represents the angle step, ω x Represents the number of the first grid; ω y Represents the number of the second grid; ω θ is the search angle in the map, W x Indicates the actual search distance in the x-axis direction; W y Indicates the actual search distance in the y-axis direction; W θ Indicates the actual search angle.

[0064] In the process of obtaining the angle step length, the expression of the angle step length mentioned in the present invention is:

[0065]

[0066]

[0067] Among them, d max Indicates the number of data points; h k Represents a set of scanned data points.

[0068] The present invention also discloses a positioning and mapping system based on dual single-line laser radars, comprising:

[0069] A data acquisition module, used for respectively acquiring first radar data of a first single-line laser radar and second radar data of a second single-line laser radar by setting dual single-line laser radars at two ends of the mobile robot, wherein the dual single-line laser radars are used to represent two single-line laser radars set at different ends of the mobile machine;

[0070] A data processing module, used for fusing the first radar data and the second radar data based on the relative position data of the two single-line laser radars to generate point cloud data;

[0071] The map generation module is used to perform point cloud matching based on point cloud data, obtain sub-map data and relative posture data corresponding to the sub-map data, and generate a first positioning map of the mobile robot.

[0072] The technical solution designed by the present invention adopts dual laser radars to obtain environmental information to generate point cloud data, form a sub-map, obtain the relative posture information between the mobile robot and the sub-map through point cloud matching, and after the point cloud data is updated, calculate the position of the latest point cloud data in the constructed map through loop detection to update the two-dimensional grid map.

[0073] In the setting of the single-line laser radar of the present invention, according to the external shape structure of the mobile robot, more than two single-line laser radars can be set, and they are set at a certain angle or position relationship. The angle between the single-line laser radars is between 60-180°. The method for constructing a positioning map for a mobile robot with more than two single-line laser radars is basically the same as the construction process of two single-line laser radars, except that in the process of generating the best matching result, based on the initial data points and according to the scanning matching strategy of the two single-line laser radars, each two single-line laser radars are scanned and matched to generate the best matching result. This design improves the calculation speed and accuracy of the map scanning and updating of more than two single-line laser radars.

[0074] Embodiment 1: The present invention discloses a method for positioning and mapping a mobile robot, and the specific steps are as follows:

[0075] Step S1: First, install a laser radar at two different positions of the mobile robot to obtain laser radar data.

[0076] Step S2: Fusing the LiDAR data to obtain each frame of point cloud data.

[0077] Step S3: A certain number of laser point cloud data frames are matched to obtain sub-map data and relative pose data.

[0078] Step S4: When the latest LiDAR scan data frame is updated, the acquired pose information will be matched with all pose information in the existing submap by scanning. If the loop closure detection finds the best match, it will be added to the optimization, the poses in all submaps will be optimized, and the new map will be completed. Loop closure detection includes submap pose optimization and scan matching. Scan matching uses the branch and bound scan matching method, and the process is as follows:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Where ξ is the laser scanning data point, each scanning data point is a pose information, including the laser radar rotation angle information ξ θ (rad) and the relative position information of the scan data point and the laser radar. The relative position information includes the position of the scan point relative to the laser radar on the x-axis ξ x (m) and the position information of the scanning point relative to the laser radar on the y-axis ξ y (m); ξ * is the optimal matching scan data point, K is the number of scan data points in the search space; M nearest is the grid value corresponding to the grid point closest to the new scan data point, dimensionless, T ξ h k represents the position of the laser radar scan data point in the sub-map coordinate system, W is the search space in the actual environment, where d max is the number of data points contained in the maximum scan data point set; h k is the set of scanned data points, δ θ is the angle step size, rad; W x is the actual search distance in the x-axis direction, m; W y is the actual search distance in the y-axis direction, m; W θ is the actual search angle, rad; r is the side length of a grid in the grid map, m; ω x is the number of grids in the x-axis direction of the map; ω yis the number of grids in the y-axis direction of the map; ω θ is the search angle in the map, rad, ξ 0 is the initial data point, j x j is the number of grids from the scanned data point in the x-axis direction of the map to the coordinate origin of the map; y j is the number of grids from the coordinate origin of the scanned data point in the y-axis direction of the map; θ is the angle between the scanned data point in the map and the origin of the map coordinates, rad; is the number of rasters to include in the search space of the map.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0090] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A positioning and mapping method for a mobile robot, characterized in that: The following steps are involved: The single-line laser radar is arranged on the mobile robot, and first radar data of the first single-line laser radar and second radar data of the second single-line laser radar are respectively acquired; Based on the relative position data between the single-line laser radars, the first radar data and the second radar data are fused to generate point cloud data; Based on the point cloud data, point cloud matching is performed to obtain sub-map data and relative posture data corresponding to the sub-map data, and to generate a first positioning map of the mobile robot; In the process of generating the positioning map of the mobile robot, the posture data obtained by scanning the two single-line laser radars are collected and matched with the relative posture data, and the first positioning map is optimized according to the best matching result to generate a second positioning map; In the process of optimizing the positioning map, updating the first positioning map according to the second positioning map; In the process of obtaining the best matching result, based on the branch and bound scanning matching method, the best matching result is generated by collecting the posture data and matching it with the relative posture data; In the process of obtaining the best matching result, the steps of the branch and bound scanning matching method include: Obtaining a maximum scanning data point set of the two single-line laser radars, obtaining an angle step size according to the number of data points in the maximum scanning data point set, and generating a search angle in a map; Obtaining a first grid number of the distance between the scanned data point in the x-axis direction and the coordinate origin in the map, and a second grid number of the distance between the scanned data point in the y-axis direction and the coordinate origin in the first positioning map; Generate a third grid number in the search space of the map according to the search angle, the first grid number, and the second grid number; Based on the third number of grids and the side length of each grid, the search space is generated, and scan matching is performed according to the initial data points used for scanning and the laser scanning data points to obtain the best matching result and the data points corresponding to the best matching result; The second positioning map is generated according to the data points.

2. A positioning and mapping method for a mobile robot according to claim 1, characterized in that: The data point corresponding to the best matching result is expressed as: Among them, ξ * represents the best matching scan data point, K represents the number of scan data points in the search space, ξ represents the laser scan data point, M nearest Indicates the grid value corresponding to the grid point closest to the new scan data point, T ξ h k represents the position of the LiDAR scan data point in the sub-map coordinate system, and W represents the search space in the actual environment.

3. A positioning and mapping method for a mobile robot according to claim 2, characterized in that: In the process of generating the search space, the search space is expressed as: Among them, ξ0 represents the initial data point, j x Indicates the number of grids from the scanned data point in the x-axis direction of the map to the coordinate origin of the map; j y Indicates the number of grids from the coordinate origin of the scanned data point in the y-axis direction of the map; j θ Indicates the angle between the scanned data point in the map and the origin of the map coordinates; It represents the number of third grids included in the search space of the map, and r represents the side length of a grid in the grid map.

4. A positioning and mapping method for a mobile robot according to claim 3, characterized in that: In the process of generating the third grid quantity, the third grid quantity is expressed as: in, δ θ represents the angle step, ω x Represents the number of the first grid; ω y Represents the number of the second grid; ω θ is the search angle in the map, W x Indicates the actual search distance in the x-axis direction; W y Indicates the actual search distance in the y-axis direction; W θ Indicates the actual search angle.

5. A positioning and mapping method for a mobile robot according to claim 4, characterized in that: In the process of obtaining the angle step, the expression of the angle step is: Among them, d max Indicates the number of data points; h k Represents a set of scanned data points.

6. A positioning and mapping system based on dual single-line laser radar, used to implement a positioning and mapping method for a mobile robot as claimed in claim 1, characterized in that: include: A data acquisition module, used for respectively acquiring first radar data of a first single-line laser radar and second radar data of a second single-line laser radar by arranging the dual single-line laser radars at two ends of the mobile robot, wherein the dual single-line laser radars are used to represent two single-line laser radars arranged at different ends of the mobile machine; A data processing module, used for fusing the first radar data and the second radar data based on the relative position data of the two single-line laser radars to generate point cloud data; The map generation module is used to perform point cloud matching based on the point cloud data, obtain sub-map data and relative posture data corresponding to the sub-map data, and generate a first positioning map of the mobile robot.

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