YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments
Visual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes can incorrectly add moving feature points to the camera pose calculation, which leads to low accuracy and poor robustness of pose estimation. In this paper, we propose a visual SLAM algorithm based on object detection a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/18/2872 |
| _version_ | 1797489146001883136 |
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| author | Lisang Liu Jiangfeng Guo Rongsheng Zhang |
| author_facet | Lisang Liu Jiangfeng Guo Rongsheng Zhang |
| author_sort | Lisang Liu |
| collection | DOAJ |
| description | Visual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes can incorrectly add moving feature points to the camera pose calculation, which leads to low accuracy and poor robustness of pose estimation. In this paper, we propose a visual SLAM algorithm based on object detection and static probability update strategy for dynamic scenes, named YKP-SLAM. Firstly, we use the YOLOv5 target detection algorithm and the improved K-means clustering algorithm to segment the image into static regions, suspicious static regions, and dynamic regions. Secondly, the static probability of feature points in each region is initialized and used as weights to solve for the initial camera pose. Then, we use the motion constraints and epipolar constraints to update the static probability of the feature points to solve the final pose of the camera. Finally, it is tested on the TUM RGB-D dataset. The results show that the YKP-SLAM algorithm proposed in this paper can effectively improve the pose estimation accuracy. Compared with the ORBSLAM2 algorithm, the absolute pose estimation accuracy is improved by 56.07% and 96.45% in low dynamic scenes and high dynamic scenes, respectively, and the best results are almost obtained compared with other advanced dynamic SLAM algorithms. |
| first_indexed | 2024-03-10T00:12:20Z |
| format | Article |
| id | doaj.art-b3b087ada05c4e21ba130e6407d1ca32 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:12:20Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b3b087ada05c4e21ba130e6407d1ca322023-11-23T15:58:02ZengMDPI AGElectronics2079-92922022-09-011118287210.3390/electronics11182872YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic EnvironmentsLisang Liu0Jiangfeng Guo1Rongsheng Zhang2School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, ChinaVisual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes can incorrectly add moving feature points to the camera pose calculation, which leads to low accuracy and poor robustness of pose estimation. In this paper, we propose a visual SLAM algorithm based on object detection and static probability update strategy for dynamic scenes, named YKP-SLAM. Firstly, we use the YOLOv5 target detection algorithm and the improved K-means clustering algorithm to segment the image into static regions, suspicious static regions, and dynamic regions. Secondly, the static probability of feature points in each region is initialized and used as weights to solve for the initial camera pose. Then, we use the motion constraints and epipolar constraints to update the static probability of the feature points to solve the final pose of the camera. Finally, it is tested on the TUM RGB-D dataset. The results show that the YKP-SLAM algorithm proposed in this paper can effectively improve the pose estimation accuracy. Compared with the ORBSLAM2 algorithm, the absolute pose estimation accuracy is improved by 56.07% and 96.45% in low dynamic scenes and high dynamic scenes, respectively, and the best results are almost obtained compared with other advanced dynamic SLAM algorithms.https://www.mdpi.com/2079-9292/11/18/2872Visual SLAMdynamic sceneYOLOv5K-means clusteringprobability update |
| spellingShingle | Lisang Liu Jiangfeng Guo Rongsheng Zhang YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments Electronics Visual SLAM dynamic scene YOLOv5 K-means clustering probability update |
| title | YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments |
| title_full | YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments |
| title_fullStr | YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments |
| title_full_unstemmed | YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments |
| title_short | YKP-SLAM: A Visual SLAM Based on Static Probability Update Strategy for Dynamic Environments |
| title_sort | ykp slam a visual slam based on static probability update strategy for dynamic environments |
| topic | Visual SLAM dynamic scene YOLOv5 K-means clustering probability update |
| url | https://www.mdpi.com/2079-9292/11/18/2872 |
| work_keys_str_mv | AT lisangliu ykpslamavisualslambasedonstaticprobabilityupdatestrategyfordynamicenvironments AT jiangfengguo ykpslamavisualslambasedonstaticprobabilityupdatestrategyfordynamicenvironments AT rongshengzhang ykpslamavisualslambasedonstaticprobabilityupdatestrategyfordynamicenvironments |