DOE: a dynamic object elimination scheme based on geometric and semantic constraints
In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2293460 |
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| _version_ | 1849703557019729920 |
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| author | Yanli Liu Siyi Chen Heng Zhang Neal N. Xiong Wei Liang |
| author_facet | Yanli Liu Siyi Chen Heng Zhang Neal N. Xiong Wei Liang |
| author_sort | Yanli Liu |
| collection | DOAJ |
| description | In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy. |
| format | Article |
| id | doaj-art-0bb7a003263e43f2a2a6ede23bc0c99b |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0bb7a003263e43f2a2a6ede23bc0c99b2025-08-20T03:17:13ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.2293460DOE: a dynamic object elimination scheme based on geometric and semantic constraintsYanli Liu0Siyi Chen1Heng Zhang2Neal N. Xiong3Wei Liang4School of Electronic Information, Shanghai Dianji University, Shanghai, People's Republic of ChinaSchool of Electronic Information, Shanghai Dianji University, Shanghai, People's Republic of ChinaSchool of Electronic Information, Shanghai Dianji University, Shanghai, People's Republic of ChinaDepartment of Mathematics and Computer Science, Sul Ross State University, Alpine, TX, USASchool of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People's Republic of ChinaIn this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy.https://www.tandfonline.com/doi/10.1080/09540091.2023.2293460Simultaneous localisation and mappingfeature pointdynamic environmentsemantic segmmentationmobile robots |
| spellingShingle | Yanli Liu Siyi Chen Heng Zhang Neal N. Xiong Wei Liang DOE: a dynamic object elimination scheme based on geometric and semantic constraints Connection Science Simultaneous localisation and mapping feature point dynamic environment semantic segmmentation mobile robots |
| title | DOE: a dynamic object elimination scheme based on geometric and semantic constraints |
| title_full | DOE: a dynamic object elimination scheme based on geometric and semantic constraints |
| title_fullStr | DOE: a dynamic object elimination scheme based on geometric and semantic constraints |
| title_full_unstemmed | DOE: a dynamic object elimination scheme based on geometric and semantic constraints |
| title_short | DOE: a dynamic object elimination scheme based on geometric and semantic constraints |
| title_sort | doe a dynamic object elimination scheme based on geometric and semantic constraints |
| topic | Simultaneous localisation and mapping feature point dynamic environment semantic segmmentation mobile robots |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2293460 |
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