High-precision SLAM positioning data processing method based on environmental supplementary features

Abstract

The present invention relates to the field of precision SLAM positioning data processing, and particularly to a high-precision SLAM positioning data processing method based on environmental supplementary features, including: obtaining corresponding associated environmental position features by using real-time SLAM data; establishing environmental supplementary features by using the associated environmental position features; obtaining a high-precision SLAM positioning data processing result by using the environmental supplementary features and real-time SLAM data. By extracting key features in the SLAM data, establishing a data sequence and analyzing it in combination with time, after obtaining environmental influencing factors, it is equivalent to internal fluctuations in the data and adjusted and improved to ensure stable and efficient output of positioning data.

Description

Technical Field

[0001] The present invention relates to the field of precision SLAM positioning data processing, and particularly to a high-precision SLAM positioning data processing method based on environmental supplementary features. Background Art

[0002] SLAM is Simultaneous Localization and Mapping. To solve the problem of robot autonomous navigation, as a theoretical method, it has been proposed and solved in various different forms and is widely applied to environments such as indoor, outdoor, underwater, and aerial. However, real-time positioning data often has errors, and there is a certain degree of lack of solutions for the sources and corrections of the errors. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a high-precision SLAM positioning data processing method based on environmental supplementary features, which quickly analyzes and processes positioning data by establishing data features and associated time series data.

[0004] To achieve the above object, the present invention provides a high-precision SLAM positioning data processing method based on environmental supplementary features, including:

[0005] S1. Obtain corresponding associated environmental position features by using real-time SLAM data;

[0006] S2. Establish environmental supplementary features by using the associated environmental position features;

[0007] S3. Obtain the processing result of high-precision SLAM positioning data by using the environmental supplementary features and real-time SLAM data.

[0008] Preferably, the obtaining of the corresponding associated environmental position features by using real-time SLAM data includes:

[0009] Obtain the corresponding real-time horizontal coordinates according to the real-time SLAM data;

[0010] Obtain the corresponding real-time vertical coordinates according to the real-time SLAM data;

[0011] Obtain the real-time horizontal motion vector according to the real-time horizontal coordinates and the horizontal coordinates of the adjacent previous moment;

[0012] Obtain the real-time vertical motion vector according to the real-time vertical coordinates and the vertical coordinates of the adjacent previous moment;

[0013] Use the real-time horizontal motion vector and the real-time vertical motion vector as the associated environmental position features.

[0014] Furthermore, the establishment of environmental supplementary features by using the associated environmental position features includes:

[0015] S2-1. Establish real-time trajectory change features using the associated environmental location features;

[0016] S2-2. Obtain environmental supplementary features using the associated environmental location features and real-time trajectory change features.

[0017] Furthermore, establishing real-time trajectory change features using the associated environmental location features includes:

[0018] S2-1-1. Use the corresponding moment of the real-time SLAM data as the standard moment t;

[0019] S2-1-2. Obtain the associated environmental location features at the standard moment t, moment t-1, and moment t-2 respectively;

[0020] S2-1-3. Establish a real-time retrospective trajectory using the associated environmental location features at the standard moment t, moment t-1, and moment t-2 corresponding to the real-time SLAM data;

[0021] S2-1-4. Establish a real-time horizontal motion trend and a real-time vertical motion trend using the associated environmental location features at the standard moment t, moment t-1, and moment t-2 respectively;

[0022] S2-1-5. Use the real-time retrospective trajectory, real-time horizontal motion trend, and real-time vertical motion trend as real-time trajectory change features.

[0023] Furthermore, obtaining environmental supplementary features using the associated environmental location features and real-time trajectory change features includes:

[0024] S2-2-1. Judge whether there is a correspondence between the associated environmental location features and the real-time retrospective trajectory of the real-time trajectory change features. If so, execute S2-2-2; otherwise, return to S2-1-3;

[0025] S2-2-2. Judge whether there is a correspondence between the associated environmental location features and the real-time horizontal motion trend of the real-time trajectory change features. If so, execute S2-2-3; otherwise, return to S2-1-4;

[0026] S2-2-3. Judge whether there is a correspondence between the associated environmental location features and the real-time vertical motion trend of the real-time trajectory change features. If so, use the real-time trajectory change features as environmental supplementary features; otherwise, return to S2-1-5.

[0027] Furthermore, obtaining the high-precision SLAM positioning data processing result using the environmental supplementary features and real-time SLAM data includes:

[0028] S3-1. Obtain the corresponding historical environmental supplementary features according to the environmental supplementary features;

[0029] S3-2. Obtain the corresponding historical backtracking trajectory, historical horizontal motion trend, and historical vertical motion trend according to the historical environment supplement features using the real-time horizontal coordinates and real-time vertical coordinates corresponding to the real-time SLAM data;

[0030] S3-3. Determine whether the real-time backtracking trajectory corresponding to the environment supplement feature is consistent with the historical backtracking trajectory. If so, use the real-time SLAM data as the processing result of the high-precision SLAM positioning data. Otherwise, execute S3-4;

[0031] S3-4. Use the difference between the real-time horizontal motion trend and the historical horizontal motion trend corresponding to the environment supplement feature as the horizontal motion trend threshold;

[0032] S3-5. Use the difference between the real-time vertical motion trend and the historical vertical motion trend corresponding to the environment supplement feature as the vertical motion trend threshold;

[0033] S3-6. Use the horizontal motion trend threshold and the vertical motion trend threshold as the processing result of the high-precision SLAM positioning data.

[0034] Compared with the closest prior art, the beneficial effects of the present invention are as follows:

[0035] By extracting key features in the SLAM data, establishing a data sequence and analyzing it in combination with time, after obtaining the environmental influencing factors, it is equivalent to the internal fluctuation of the data and is adjusted and improved to ensure the stable and efficient output of the positioning data. Description of the Drawings

[0036] Figure 1 It is a flowchart of a method for processing high-precision SLAM positioning data based on environmental supplement features provided by the present invention. Detailed Embodiments

[0037] The following further details the specific embodiments of the present invention with reference to the drawings.

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] Embodiment 1:

[0040] The present invention provides a high-precision SLAM positioning data processing method based on environmental supplementary features, as follows Figure 1 shown, including:

[0041] S1. Obtain corresponding associated environmental position features by using real-time SLAM data;

[0042] S2. Establish environmental supplementary features by using the associated environmental position features;

[0043] S3. Obtain the processing result of high-precision SLAM positioning data by using the environmental supplementary features and real-time SLAM data.

[0044] S1 specifically includes:

[0045] S1-1. Obtain corresponding real-time horizontal coordinates according to the real-time SLAM data;

[0046] S1-2. Obtain corresponding real-time vertical coordinates according to the real-time SLAM data;

[0047] S1-3. Obtain a real-time horizontal motion vector according to the real-time horizontal coordinates and the horizontal coordinates of the adjacent previous moment;

[0048] S1-4. Obtain a real-time vertical motion vector according to the real-time vertical coordinates and the vertical coordinates of the adjacent previous moment;

[0049] S1-5. Use the real-time horizontal motion vector and the real-time vertical motion vector as the associated environmental position features.

[0050] S2 specifically includes:

[0051] S2-1. Establish real-time trajectory change features by using the associated environmental position features;

[0052] S2-2. Obtain environmental supplementary features by using the associated environmental position features and the real-time trajectory change features.

[0053] S2-1 specifically includes:

[0054] S2-1-1. Use the corresponding moment of the real-time SLAM data as the standard moment t;

[0055] S2-1-2. Respectively obtain the associated environmental position features at the standard moment t, the moments t-1 and t-2;

[0056] S2-1-3. Establish a real-time retrospective trajectory by using the associated environmental position features at the standard moment t, the moments t-1 and t-2 corresponding to the real-time SLAM data;

[0057] S2-1-4. Establish the real-time horizontal motion trend and real-time vertical motion trend respectively using the associated environmental position features at the standard time t, the time t-1, and the time t-2;

[0058] S2-1-5. Use the real-time retrospective trajectory, the real-time horizontal motion trend, and the real-time vertical motion trend as the real-time trajectory change features.

[0059] S2-2 specifically includes:

[0060] S2-2-1. Determine whether there is a correspondence between the associated environmental position feature and the real-time retrospective trajectory of the real-time trajectory change feature. If so, execute S2-2-2; otherwise, return to S2-1-3;

[0061] S2-2-2. Determine whether there is a correspondence between the associated environmental position feature and the real-time horizontal motion trend of the real-time trajectory change feature. If so, execute S2-2-3; otherwise, return to S2-1-4;

[0062] S2-2-3. Determine whether there is a correspondence between the associated environmental position feature and the real-time vertical motion trend of the real-time trajectory change feature. If so, use the real-time trajectory change feature as the environmental supplementary feature; otherwise, return to S2-1-5.

[0063] S3 specifically includes:

[0064] S3-1. Obtain the corresponding historical environmental supplementary feature according to the environmental supplementary feature;

[0065] S3-2. Use the real-time horizontal coordinates and real-time vertical coordinates corresponding to the real-time SLAM data to obtain the corresponding historical retrospective trajectory, historical horizontal motion trend, and historical vertical motion trend according to the historical environmental supplementary feature;

[0066] S3-3. Determine whether the real-time retrospective trajectory corresponding to the environmental supplementary feature is consistent with the historical retrospective trajectory. If so, use the real-time SLAM data as the processing result of the high-precision SLAM positioning data; otherwise, execute S3-4;

[0067] S3-4. Use the difference between the real-time horizontal motion trend and the historical horizontal motion trend corresponding to the environmental supplementary feature as the horizontal motion trend threshold;

[0068] S3-5. Use the difference between the real-time vertical motion trend and the historical vertical motion trend corresponding to the environmental supplementary feature as the vertical motion trend threshold;

[0069] S3-6. Use the horizontal motion trend threshold and the vertical motion trend threshold as the processing result of the high-precision SLAM positioning data.

[0070] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0071] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realize the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A high-precision SLAM positioning data processing method based on environmental supplementary features, characterized in that: include: S1, using real-time SLAM data to obtain the corresponding associated environment position features; S1-1, obtaining corresponding real-time horizontal coordinates according to the real-time SLAM data; S1-2, acquiring corresponding real-time vertical coordinates according to the real-time SLAM data; S1-3, using the real-time horizontal coordinate to obtain a real-time horizontal motion vector according to the horizontal coordinate of the previous adjacent moment; S1-4, using the real-time vertical coordinate to obtain a real-time vertical motion vector according to the vertical coordinate of the previous adjacent moment; S1-5, using the real-time horizontal motion vector and the real-time vertical motion vector as associated environment position features; S2, using the associated environmental location features to establish environmental supplementary features; S2-1, using the associated environment position feature to establish a real-time trajectory change feature; S2-2, using the associated environment position feature and the real-time trajectory change feature to obtain the environment supplement feature; S3. Utilize the environmental supplementary features and real-time SLAM data to obtain high-precision SLAM positioning data processing results.

2. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 1, characterized in that, Using the associated environment position feature to establish a real-time trajectory change feature includes: S2-1-1, using the time corresponding to the real-time SLAM data as the standard time t; S2-1-2, respectively obtaining the associated environmental location features at the standard time t, time t-1 and time t-2; S2-1-3, using the real-time SLAM data corresponding to the associated environmental position features at the standard time t, time t-1 and time t-2 to establish a real-time backtracking trajectory; S2-1-4, using the associated environmental position features at the standard time t, time t-1 and time t-2 to respectively establish a real-time horizontal movement trend and a real-time vertical movement trend; S2-1-5. Utilize the real-time backtracking trajectory, the real-time horizontal movement trend, and the real-time vertical movement trend as real-time trajectory change features.

3. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 2, characterized in that, The environmental supplementary features obtained by using the associated environmental position features and real-time trajectory change features include: S2-2-1, determine whether the associated environment location feature corresponds to the real-time backtracking trajectory of the real-time trajectory change feature, if so, execute S2-2-2, otherwise, return to S2-1-3; S2-2-2, determine whether the associated environment position feature corresponds to the real-time horizontal movement trend of the real-time trajectory change feature, if so, execute S2-2-3, otherwise, return to S2-1-4; S2-2-3, determine whether the associated environment position feature corresponds to the real-time vertical movement trend of the real-time trajectory change feature, if so, use the real-time trajectory change feature as the environment supplement feature, otherwise, return to S2-1-5.

4. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 1, characterized in that, The high-precision SLAM positioning data processing results obtained by using the environmental supplementary features and real-time SLAM data include: S3-1, obtaining corresponding historical environmental supplementary features according to the environmental supplementary features; S3-2, using the real-time horizontal coordinates and real-time vertical coordinates of the real-time SLAM data to obtain the corresponding historical backtracking trajectory, historical horizontal movement trend and historical vertical movement trend according to the historical environment supplementary features; S3-3, determining whether the real-time backtracking trajectory corresponding to the environmental supplementary feature is consistent with the historical backtracking trajectory, if so, using the real-time SLAM data as the high-precision SLAM positioning data processing result, otherwise, executing S3-4; S3-4, using the difference between the real-time horizontal motion trend and the historical horizontal motion trend corresponding to the environmental supplementary feature as a horizontal motion trend threshold; S3-5, using the difference between the real-time vertical movement trend and the historical vertical movement trend corresponding to the environmental supplementary feature as a vertical movement trend threshold; S3-6. Utilize the horizontal motion trend threshold and the vertical motion trend threshold as high-precision SLAM positioning data processing results.

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