A multi-region map construction method and system based on adaptive adjustment

Abstract

The present invention relates to a method and system for constructing a multi-region map based on adaptive adjustment, belonging to the field of map construction. The method comprises collecting boundary information, modeling the boundaries of the multi-region map based on the boundary information, and obtaining the multi-region map boundaries, wherein the multi-region map boundary modeling utilizes a curvature adaptive correction protocol and a terrain similarity correction protocol; obtaining cross-map splicing credibility based on the multi-region map boundaries and marking the areas to be connected; and constructing a connecting plank road, connecting the areas to be connected via the connecting plank road, thereby achieving complete regional map construction. The present invention achieves highly efficient and adaptable multi-region map construction, effectively connecting multiple regions.

Description

Technical Field

[0001] The present invention belongs to the technical field of map construction, and in particular relates to a multi-region map construction method and system based on adaptive adjustment. Background Art

[0002] With the popularization of outdoor automatic tools, more and more people are beginning to use automated outdoor automatic tools to replace traditional mechanical equipment that requires manual operation, thereby greatly reducing the labor intensity of people's outdoor work and improving work efficiency.

[0003] However, existing outdoor automatic tools can only perform automated operations on a complete and independent area and / or site when working. When there are multiple relatively independent areas and they are not connected to each other, existing outdoor automatic tools cannot effectively build a working map connecting independent area A and independent area B, resulting in the automatic robot being unable to automatically move from independent area A to independent area B for continuous work.

[0004] Currently, when constructing a work map for discontinuous work areas (i.e., multiple independent sub-areas), high-precision positioning equipment and methods are often used to remotely control a robot to walk across the entire work area (including all independent sub-areas and the connections between them). The resulting data is then used to construct a map of the entire work area. This approach has many practical problems, primarily as follows:

[0005] (1) Poor terrain adaptability: Unable to identify sudden changes in complex terrain (such as spiral ramps, etc.).

[0006] (2) Low degree of splicing automation: It relies on manual intervention and cannot dynamically evaluate the topological relationship of multiple regions.

[0007] (3) Static path planning: time cost and dynamic environmental factors such as obstacles are not integrated. Summary of the Invention

[0008] In order to solve the above problems existing in the prior art, the present invention provides a multi-region map construction method and system based on adaptive adjustment.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] A method for constructing a multi-region map based on adaptive regulation, wherein the implementation of the multi-region map construction method comprises the following steps:

[0011] S1: Collect boundary information, perform multi-region map boundary modeling based on the boundary information, and obtain multi-region map boundaries, wherein the multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol;

[0012] S2: Obtaining cross-map splicing credibility based on the multi-region map boundaries and marking the areas to be connected;

[0013] S3: Constructing a connecting plank road, connecting the areas to be connected through the connecting plank road, and realizing the construction of a complete regional map.

[0014] Preferably, the step S1 specifically includes:

[0015] S101: Continuously collecting the boundary information of each area, wherein the boundary information includes real-time coordinates, heading angle, and pressure altitude;

[0016] S102: acquiring the compound curvature of each region based on the boundary information, and triggering the curvature adaptive correction protocol according to the compound curvature;

[0017] S103: Calculating terrain similarity according to the compound curvature, and triggering the terrain similarity correction protocol when the terrain similarity exceeds a preset threshold.

[0018] Preferably, the triggering of the curvature adaptive correction protocol in step S102 specifically includes:

[0019] S102-1: Obtaining the heading angle difference between adjacent sampling points and the actual moving distance between adjacent sampling points according to the real-time coordinates and the heading angle, and calculating the geometric curvature;

[0020] S102-2: Obtaining the second-order derivative of elevation change between adjacent sampling points based on the pressure altitude and the real-time coordinates, and calculating terrain curvature;

[0021] S102-3: Normalizing the geometric curvature and the terrain curvature to obtain the composite curvature;

[0022] S102-4: When the composite curvature exceeds a preset threshold range, triggering the curvature adaptive correction protocol.

[0023] Preferably, the calculation formula of the geometric curvature in step S102-1 is: ,in, is the heading angle difference between adjacent sampling points, is the actual moving distance between adjacent sampling points.

[0024] Preferably, the calculation formula of the terrain curvature in step S102-2 is: , where λ is the terrain influence factor, is the second-order derivative of altitude change, and h is the pressure altitude.

[0025] Preferably, the marking of the area to be connected in step S2 specifically includes:

[0026] S201: extracting area information of each area according to the multi-area map boundary, wherein the area information includes the perimeter of the area boundary, the number of area boundary corners, and the area of the area;

[0027] S202: Generate a regional feature vector based on the regional information;

[0028] S203: Obtaining the relative rotation angle between regions;

[0029] S204: Obtaining the cross-map splicing credibility based on the regional feature vector and the relative rotation angle between the regions;

[0030] S205: When the cross-map splicing credibility between two areas exceeds a preset threshold, they are marked as the areas to be connected.

[0031] Preferably, the mathematical description of the cross-map splicing credibility in step S204 is: ,in, is the cross-map splicing credibility between region i and region j, is the regional feature vector of region j, is the regional characteristic vector of region i, is the relative rotation angle between regions i and j, is the angle tolerance threshold.

[0032] Preferably, the selection of the connecting plank road in step S3 specifically includes:

[0033] S301: Obtain the obstruction distance from the path point to the nearest obstacle O, and construct a spatial cost function based on the obstruction distance;

[0034] S302: Acquire the current time, and construct a time cost function based on the current time;

[0035] S303: Obtaining a dynamic path cost according to the spatial cost function and the temporal cost function, traversing all connecting channels between the areas to be connected, and selecting a connecting channel with the lowest dynamic path cost as the connecting plank road.

[0036] Preferably, the mathematical description of the space cost function is , where C(t)1 is the space cost function, L ref is the total length of the path, is the basic terrain coefficient, λ is the safety distance attenuation coefficient, and d(s,O) is the obstacle distance; the mathematical description of the time cost function is , where C(t)2 is the time cost function, μ is the time weight factor, t is the current time, and T is the length of the time period, which is 24.

[0037] A multi-region map construction system based on adaptive regulation, used to execute the multi-region map construction method described above, comprising a boundary modeling module, a to-be-connected region marking module, and a region map construction module;

[0038] The boundary modeling module is used to collect boundary information, and perform multi-region map boundary modeling based on the boundary information to obtain multi-region map boundaries. The multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol;

[0039] The to-be-connected region marking module is used to obtain the cross-map splicing credibility according to the multi-region map boundaries and mark the to-be-connected region;

[0040] The regional map construction module is used to construct a connecting plank road, and connect the areas to be connected through the connecting plank road to achieve complete regional map construction.

[0041] The beneficial effects of the present invention are:

[0042] (1) Curvature adaptive sampling: Dynamically adjust the sampling frequency based on geometry and terrain curvature to reduce redundant data and avoid missing complex terrain.

[0043] (2) Cross-region credibility model: integrates regional feature vectors and rotation angle deviations to quantify the feasibility of splicing.

[0044] (3) Dynamic path cost function: Integrate spatial obstacles and time factors to achieve multi-objective optimization path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0046] Figure 1 The present invention is a flowchart of the steps of a multi-region map construction method based on adaptive adjustment. DETAILED DESCRIPTION

[0047] In order to better understand the present invention, various aspects of the present invention will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention and are not intended to limit the scope of the present invention in any way. Throughout the specification, the expression "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the terms "roughly", "approximately" and similar terms are used as terms to indicate approximate values, rather than as terms to indicate degree, and are intended to illustrate inherent deviations in measurements or calculated values that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0048] It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open rather than closed expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention." And, the term "exemplary" is intended to refer to an example or illustration.

[0049] Unless otherwise defined, all terms used herein (including engineering and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this invention pertains. It should also be understood that, unless otherwise expressly stated herein, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense.

[0050] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] Example 1:

[0052] See also Figure 1 , a multi-region map construction method based on adaptive adjustment, comprising:

[0053] S1: Collect boundary information, perform multi-region map boundary modeling based on the boundary information, and obtain multi-region map boundaries, wherein the multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol;

[0054] S2: Obtaining cross-map splicing credibility based on the multi-region map boundaries and marking the areas to be connected;

[0055] S3: Construct a connecting plank road, connect the areas to be connected through the connecting plank road, and realize the construction of a complete regional map, so that automatic tools and other equipment can reach any point in area j from any point in area i.

[0056] In this embodiment, based on the boundary information, multi-region map boundary modeling is performed respectively to obtain the multi-region map boundary, which can be specifically implemented by the following steps:

[0057] S101: Continuously collect the boundary information of each area using mapping equipment (GPS, barometer, etc.), that is, move a robot or handheld mapping equipment along the edge of each area and record the boundary information at regular intervals / distances. The boundary information includes real-time coordinates, heading angle (assuming true north is 0° and increases clockwise), and barometric altitude;

[0058] S102: acquiring the compound curvature of each region based on the boundary information, and triggering the curvature adaptive correction protocol according to the compound curvature;

[0059] S103: Calculate terrain similarity based on the compound curvature. The calculation formula is: , where S i is the terrain similarity of region i, M is the current number of sampling points, is the time series attenuation weight, is the composite curvature of the mth sampling point in region i, is the composite curvature of the m-1th sampling point in region i. When the terrain similarity of region i exceeds a preset threshold, it is possible that the terrain similarity of previous sampling points in region i is extremely high (i.e., there may be a sampling error, for example, the information collected from five consecutive sampling points is highly similar). In this case, the terrain similarity correction protocol is triggered to re-collect the boundary information of the abnormal sampling point.

[0060] S104: performing multi-region map boundary modeling based on the boundary information, that is, drawing boundary maps of each region respectively according to the collected boundary information.

[0061] In this embodiment, the compound curvature of each region is obtained based on the boundary information, and the curvature adaptive correction protocol is triggered according to the compound curvature. Specifically, the following steps can be performed:

[0062] S102-1: Obtain the heading angle difference between adjacent sampling points and the actual moving distance (non-straight line distance) of adjacent sampling points according to the real-time coordinates and the heading angle, and calculate the geometric curvature. The calculation formula is: ,in, is the heading angle difference between adjacent sampling points (positive value), is the actual moving distance between adjacent sampling points, in meters;

[0063] S102-2: Obtain the second-order derivative of the elevation change between adjacent sampling points based on the pressure altitude and the real-time coordinates, and calculate the terrain curvature. The calculation formula is: , where λ is the terrain influence factor, and its empirical value is 0.7. is the second-order derivative of elevation change, h is the pressure altitude, and the unit is meter;

[0064] S102-3: Normalizing the geometric curvature and the terrain curvature to obtain the composite curvature;

[0065] S102-4: When the compound curvature exceeds the preset threshold range, the curvature adaptive correction protocol is triggered, that is, when the compound curvature in a certain area is greater than or equal to the preset threshold, the sampling is encrypted based on the default sampling frequency (for example, if the default sampling frequency is 2m / time or 2 seconds / time, it is encrypted to 1m / time or 1 second / time). When the compound curvature is less than the preset threshold, the sampling frequency is reduced based on the default sampling frequency. Example: When sampling to the corner of the area, the geometric curvature is , the terrain curvature is 0 (flat land with no elevation change), and the compound curvature is 0.707, exceeding the preset threshold of 0.5. In this case, the sampling frequency needs to be increased to prevent overlooking sudden changes in terrain. When sampling a spiraling terrain, the geometric curvature is 0.5. At this time, the terrain curvature is 0.7 × (0.3 / 1) = 0.21, and the compound curvature is 0.71. The sampling frequency also needs to be increased. For straight line segments, the sampling frequency can be appropriately reduced.

[0066] In this embodiment, the cross-map splicing credibility is obtained based on the multi-region map boundaries and the areas to be connected are marked, which can be specifically implemented by the following steps:

[0067] S201: extracting area information of each area according to the multi-area map boundary, wherein the area information includes the perimeter of the area boundary, the number of area boundary corners (only corners greater than 45° are counted), and the area of the area;

[0068] S202: Generate a regional feature vector based on the regional information, which is mathematically described as ,in, is the regional characteristic vector of region i, L i is the perimeter of the region i, Q i is the number of region boundary corners in region i, A i is the area of region i, L0 is the sum of the perimeters of the boundaries of all regions, Q0 is the sum of the number of corners of the boundaries of all regions, and A0 is the sum of the areas of all regions;

[0069] S203: Obtaining the relative rotation angle between regions. That is, taking the center of each region as the origin, the north direction as the vertical axis, and the east direction as the horizontal axis, arbitrarily selecting a reference object, obtaining the vector from the origin of each region pointing to the reference object, superimposing the two coordinate systems, and rotating the vector of one region so that it coincides with the vector of the other region. The rotation angle at this point is the relative rotation angle between the two regions (taking a positive value);

[0070] S204: Obtain the cross-map splicing credibility based on the regional feature vector and the relative rotation angle between the regions, which is mathematically described as ,in, is the cross-map splicing credibility between region i and region j, is the regional feature vector of region j, is the relative rotation angle between regions i and j, is the angle tolerance threshold, and the empirical value is 15°;

[0071] S205: When the cross-map splicing credibility between two regions exceeds a preset threshold, they are marked as the regions to be connected. Example: Region i has a boundary perimeter of 150m, 8 corners, and an area of 600m. 2 , the perimeter of region j is 170m, the number of region boundary corners is 6, and the area is 400m 2 The total perimeter of the region boundary is 1000m, the total number of region boundary corners is 24, and the total area of the region is 10000m 2 , then the regional feature vector of region i is [0.15, 0.33, 0.06], the regional feature vector of region j is [0.17, 0.25, 0.04], and the relative rotation angle between regions is 30°. The cross-map splicing reliability between the two regions is 0.133, which is greater than the preset threshold. Regions i and j are marked as a set of regions to be connected.

[0072] In this embodiment, the areas to be connected are connected by the connecting plank roads to realize the construction of a complete regional map, which can be specifically implemented by the following steps:

[0073] S301: Obtain the obstruction distance from the path point to the nearest obstacle O, and construct a spatial cost function based on the obstruction distance, which is mathematically described as , where C(t)1 is the space cost function, L ref is the total length of the path (i.e. the straight-line distance from any point in region i to any point in region j), is the basic terrain coefficient (concrete = 1.0, sand = 2.0, water = ∞, the more difficult the terrain is, the higher the basic terrain coefficient is), λ is the safety distance attenuation coefficient, the empirical value is 0.5, and d(s,O) is the obstruction distance;

[0074] S302: Obtain the current time (hour system, value ranges from 0 to 24), and construct a time cost function based on the current time, which is mathematically described as , where C(t)2 is the time cost function, μ is the time weight factor, t is the current time, and T is the time period length, which is 24;

[0075] S303: Obtain the dynamic path cost according to the space cost function and the time cost function, which is mathematically described as , where C(t) is the dynamic path cost. All connecting channels between the areas to be connected are traversed, and the connecting channel with the lowest dynamic path cost is selected as the connecting plank road. Example: At 9:00 AM, an outdoor autonomous vehicle needs to run from point A in area i to point B in area j. Two connecting channels are available: Connecting Channel 1: A straight line from point A to point B (10 meters of concrete + 5 meters of sand, with an obstacle on the concrete 13 meters away from the connecting channel); Connecting Channel 2: Detour through the outer channel (20 meters of concrete, with an obstacle 10 meters away from connecting channel 2). The dynamic path cost of connecting channel 1 is , the dynamic path cost of connecting channel 2 is Therefore, the connecting channel 2 is selected as the connecting plank road at this time.

[0076] Example 2:

[0077] A multi-region map construction system based on adaptive regulation includes a boundary modeling module, a to-be-connected region marking module and a region map construction module;

[0078] The boundary modeling module is used to collect boundary information, and perform multi-region map boundary modeling based on the boundary information to obtain multi-region map boundaries. The multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol;

[0079] The to-be-connected region marking module is used to obtain the cross-map splicing credibility according to the multi-region map boundaries and mark the to-be-connected region;

[0080] The regional map construction module is used to construct a connecting plank road, which connects the areas to be connected through the connecting plank road to achieve complete regional map construction, so that equipment such as automatic tools can reach any point in area j from any point in area i.

[0081] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A multi-region map construction method based on adaptive adjustment, characterized in that: The implementation of the multi-region map construction method includes the following steps: S1: Collect boundary information, perform multi-region map boundary modeling based on the boundary information, and obtain multi-region map boundaries, wherein the multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol; S2: Obtaining cross-map splicing credibility based on the multi-region map boundaries and marking the areas to be connected; S3: Constructing a connecting plank road, connecting the areas to be connected through the connecting plank road, and realizing the construction of a complete regional map; The step S1 specifically includes: S101: Continuously collecting the boundary information of each area, wherein the boundary information includes real-time coordinates, heading angle, and pressure altitude; S102: acquiring the compound curvature of each region based on the boundary information, and triggering the curvature adaptive correction protocol according to the compound curvature; S103: Calculating terrain similarity based on the composite curvature, triggering the terrain similarity correction protocol when the terrain similarity exceeds a preset threshold, and re-collecting boundary information of abnormal sampling points; The triggering of the curvature adaptive correction protocol in step S102 specifically includes: S102-1: Obtaining the heading angle difference between adjacent sampling points and the actual moving distance between adjacent sampling points according to the real-time coordinates and the heading angle, and calculating the geometric curvature; S102-2: Obtaining the second-order derivative of elevation change between adjacent sampling points based on the pressure altitude and the real-time coordinates, and calculating terrain curvature; S102-3: Normalizing the geometric curvature and the terrain curvature to obtain the composite curvature; S102-4: When the compound curvature exceeds a preset threshold range, the curvature adaptive correction protocol is triggered. That is, when the compound curvature in a certain area is greater than or equal to the preset threshold, sampling is increased based on the default sampling frequency. When the compound curvature is less than the preset threshold, the number of samplings is reduced based on the default sampling frequency. The marking of the area to be connected in step S2 specifically includes: S201: extracting area information of each area according to the multi-area map boundary, wherein the area information includes the perimeter of the area boundary, the number of area boundary corners, and the area of the area; S202: Generate a regional feature vector based on the regional information; S203: Obtaining the relative rotation angle between regions; S204: Obtaining the cross-map splicing credibility based on the regional feature vector and the relative rotation angle between the regions; S205: When the cross-map splicing credibility between two areas exceeds a preset threshold, they are marked as areas to be connected; The mathematical description of the cross-map splicing credibility in S204 is: ,in, is the cross-map splicing credibility between region i and region j, is the regional feature vector of region j, is the regional characteristic vector of region i, is the relative rotation angle between regions i and j, is the angle tolerance threshold.

2. The multi-region map construction method according to claim 1, characterized in that: The calculation formula of the geometric curvature in step S102-1 is: ,in, is the heading angle difference between adjacent sampling points, is the actual moving distance between adjacent sampling points.

3. The multi-region map construction method according to claim 1, characterized in that: The calculation formula of the terrain curvature in step S102-2 is: , where λ is the terrain influence factor, is the second-order derivative of altitude change, and h is the pressure altitude.

4. The multi-region map construction method according to claim 1, characterized in that: The selection of the connecting plank road in step S3 specifically includes: S301: Obtain the obstruction distance from the path point to the nearest obstacle O, and construct a spatial cost function based on the obstruction distance; S302: Acquire the current time, and construct a time cost function based on the current time; S303: Obtaining a dynamic path cost according to the spatial cost function and the temporal cost function, traversing all connecting channels between the areas to be connected, and selecting a connecting channel with the lowest dynamic path cost as the connecting plank road.

5. The multi-region map construction method according to claim 4, characterized in that: The mathematical description of the space cost function is , where C(t)1 is the space cost function, L ref is the total length of the path, is the basic terrain coefficient, λ is the safety distance attenuation coefficient, and d(s,O) is the obstacle distance; the mathematical description of the time cost function is , where C(t)2 is the time cost function, μ is the time weight factor, t is the current time, and T is the length of the time period, which is 24.

6. A multi-region map construction system based on adaptive adjustment, characterized in that: The system is applied to the multi-region map construction method according to any one of claims 1 to 5, comprising a boundary modeling module, a to-be-connected region marking module, and a region map construction module; The boundary modeling module is used to collect boundary information, and perform multi-region map boundary modeling based on the boundary information to obtain multi-region map boundaries. The multi-region map boundary modeling is equipped with a curvature adaptive correction protocol and a terrain similarity correction protocol; The to-be-connected region marking module is used to obtain the cross-map splicing credibility according to the multi-region map boundaries and mark the to-be-connected region; The regional map construction module is used to construct a connecting plank road, and connect the areas to be connected through the connecting plank road to achieve complete regional map construction.

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