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

Abstract

The invention relates to the field of precision SLAM (Simultaneous Localization and Mapping) positioning data processing, in particular to a high-precision SLAM positioning data processing method based on environmental supplementary features, which comprises the following steps: acquiring corresponding associated environmental position features by using real-time SLAM data; utilizing the associated environment position features to establish environment supplementary features; and obtaining a high-precision SLAM positioning data processing result by using the environment supplementary features and real-time SLAM data, extracting key features in the SLAM data, establishing a data sequence and combining with a moment for analysis and processing, obtaining environment influence factors, enabling the environment influence factors to be equivalent to data internal fluctuation, adjusting and improving the data internal fluctuation, and ensuring stable and efficient output of the positioning data.

Description

Technical Field

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

[0002] SLAM is a real-time positioning and mapping method. As a theoretical method, it has been proposed and solved in many different ways to solve the problem of robot autonomous navigation. It has been widely used in indoor, outdoor, underwater and aerial environments. However, real-time positioning data often has errors, and there is a certain degree of lack of solutions for the source and correction of errors. Summary of the invention

[0003] In view of the shortcomings 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 associating time data sequences.

[0004] To achieve the above object, the present invention provides a high-precision SLAM positioning data processing method based on environmental supplementary features, comprising: S1, using real-time SLAM data to obtain the corresponding associated environment position features; S2, using the associated environmental location features to establish environmental supplementary features; S3. Utilize the environmental supplementary features and real-time SLAM data to obtain high-precision SLAM positioning data processing results.

[0005] Preferably, the method of obtaining corresponding associated environment position features using real-time SLAM data includes: Acquire corresponding real-time horizontal coordinates according to the real-time SLAM data; Acquire corresponding real-time vertical coordinates according to the real-time SLAM data; Using the real-time horizontal coordinate to obtain a real-time horizontal motion vector according to the horizontal coordinate of the previous adjacent moment; Using the real-time vertical coordinates to obtain a real-time vertical motion vector according to the vertical coordinates of the previous adjacent moment; The real-time horizontal motion vector and the real-time vertical motion vector are used as associated environment position features.

[0006] Furthermore, establishing the environment supplementary feature by using the associated environment location feature includes: S2-1, using the associated environment position feature to establish a real-time trajectory change feature; S2-2, using the associated environmental position feature and the real-time trajectory change feature to obtain an environmental supplementary feature.

[0007] Furthermore, 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.

[0008] Furthermore, obtaining the environment supplementary features by using the associated environment position features and the real-time trajectory change features includes: 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.

[0009] Furthermore, 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.

[0010] Compared with the closest prior art, the present invention has the following beneficial effects: By extracting key features from SLAM data, establishing a data sequence and combining it with time for analysis and processing, and obtaining environmental influencing factors, it is equivalent to internal fluctuations in the data, which are adjusted and improved to ensure stable and efficient output of positioning data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The present invention provides a flow chart of a high-precision SLAM positioning data processing method based on environmental supplementary features. DETAILED DESCRIPTION

[0012] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Embodiment 1:

[0015] The present invention provides a high-precision SLAM positioning data processing method based on environmental supplementary features, such as Figure 1 As shown, including: S1, using real-time SLAM data to obtain the corresponding associated environment position features; S2, using the associated environmental location features to establish environmental supplementary features; S3. Utilize the environmental supplementary features and real-time SLAM data to obtain high-precision SLAM positioning data processing results.

[0016] S1 specifically includes: 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. Utilize the real-time horizontal motion vector and the real-time vertical motion vector as associated environment position features.

[0017] S2 specifically includes: S2-1, using the associated environment position feature to establish a real-time trajectory change feature; S2-2, using the associated environmental position feature and the real-time trajectory change feature to obtain an environmental supplementary feature.

[0018] S2-1 specifically 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.

[0019] S2-2 specifically includes: 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.

[0020] S3 specifically includes: 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.

[0021] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0022] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the 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 generate 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.

[0023] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0024] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0025] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection 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; S2, using the associated environmental location features to establish environmental supplementary features; 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, The method of using real-time SLAM data to obtain corresponding associated environment position features includes: Acquire corresponding real-time horizontal coordinates according to the real-time SLAM data; Acquire corresponding real-time vertical coordinates according to the real-time SLAM data; Using the real-time horizontal coordinate to obtain a real-time horizontal motion vector according to the horizontal coordinate of the previous adjacent moment; Using the real-time vertical coordinates to obtain a real-time vertical motion vector according to the vertical coordinates of the previous adjacent moment; The real-time horizontal motion vector and the real-time vertical motion vector are used as associated environment position features.

3. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 2, characterized in that, Using the associated environmental location feature to establish an environmental supplementary feature includes: S2-1, using the associated environment position feature to establish a real-time trajectory change feature; S2-2, using the associated environmental position feature and the real-time trajectory change feature to obtain an environmental supplementary feature.

4. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 3, 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.

5. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 4, 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.

6. The high-precision SLAM positioning data processing method based on environmental supplementary features as claimed in claim 3, 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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