Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes
When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/22/11548 |
| _version_ | 1797465972712407040 |
|---|---|
| author | Xinguang Zhang Ruidong Zhang Xiankun Wang |
| author_facet | Xinguang Zhang Ruidong Zhang Xiankun Wang |
| author_sort | Xinguang Zhang |
| collection | DOAJ |
| description | When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM. |
| first_indexed | 2024-03-09T18:30:12Z |
| format | Article |
| id | doaj.art-4a503d8d463447f583bd989ec29637a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:30:12Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4a503d8d463447f583bd989ec29637a22023-11-24T07:37:03ZengMDPI AGApplied Sciences2076-34172022-11-0112221154810.3390/app122211548Visual SLAM Mapping Based on YOLOv5 in Dynamic ScenesXinguang Zhang0Ruidong Zhang1Xiankun Wang2School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaWhen building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.https://www.mdpi.com/2076-3417/12/22/11548dynamic SLAMYOLOv5 networkdeep learning |
| spellingShingle | Xinguang Zhang Ruidong Zhang Xiankun Wang Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes Applied Sciences dynamic SLAM YOLOv5 network deep learning |
| title | Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes |
| title_full | Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes |
| title_fullStr | Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes |
| title_full_unstemmed | Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes |
| title_short | Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes |
| title_sort | visual slam mapping based on yolov5 in dynamic scenes |
| topic | dynamic SLAM YOLOv5 network deep learning |
| url | https://www.mdpi.com/2076-3417/12/22/11548 |
| work_keys_str_mv | AT xinguangzhang visualslammappingbasedonyolov5indynamicscenes AT ruidongzhang visualslammappingbasedonyolov5indynamicscenes AT xiankunwang visualslammappingbasedonyolov5indynamicscenes |