.png){kind=icon}
返回
DFT-VSLAM: A Dynamic Optical Flow Tracking VSLAM Method
.png){kind=icon}
OA
AI
DOI：10.1007/s10846-024-02171-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Visual Simultaneous Localization and Mapping (VSLAM) technology can provide reliable visual localization and mapping capabilities for critical tasks. Existing VSLAM can extract accurate feature points in static environments for matching and pose estimation, and then build environmental map. However, in dynamic environments, the feature points extracted by the VSLAM system will become inaccurate points as the object moves, which not only leads to tracking failure but also seriously affects the accuracy of the environmental map. To alleviate these challenges, we propose a dynamic target-aware optical flow tracking method based on YOLOv8. Firstly, we use YOLOv8 to identify moving targets in the environment, and propose a method to eliminate dynamic points in the dynamic contour region. Secondly, we use the optical flow mask method to identify dynamic feature points outside the target detection object frame. Thirdly, we comprehensively eliminate the dynamic feature points. Finally, we combine the geometric and semantic information of static map points to construct the semantic map of the environment. We used ATE (Absolute Trajectory Error) and RPE (Relative Pose Error) as evaluation metrics and compared the original method with our method on the TUM dataset. The accuracy of our method is significantly improved, especially 96.92% on walking_xyz dataset. The experimental results show that our proposed method can significantly improve the overall performance of VSLAM systems under high dynamic environments.
Keyword:
Visual SLAM
YOLOv8
Lucas-Kanade optical flow
Highly dynamic environment
Semantic mapping
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
机构
引用论文
Visual SLAM Integration With Semantic Segmentation and Deep Learning: A Review基于语义分割和深度学习的视觉SLAM集成研究综述.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏The New Economic Bilateralism in Southeast Asia: Region-Convergent or Region-Divergent?东南亚的新双边经济主义：是区域趋同还是区域发散？
PREAI
分享收藏Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8基于数据合并和YOLOv8的恶劣天气下自动驾驶目标检测SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容