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An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments
Qiuyu Zang1, Kehua Zhang2, Ling Wang2
1College of Mathematics and Computer Science, Zhejiang Normal University, Yingbin Avenue, Jinhua 321005, China.
Sensors (Basel, Switzerland)
|February 11, 2023
Summary
This study introduces an adaptive system to improve visual simultaneous localization and mapping (SLAM) accuracy in dynamic outdoor environments by intelligently selecting feature points. The method significantly reduces trajectory errors compared to existing approaches.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Visual SLAM (Simultaneous Localization and Mapping) faces accuracy challenges in dynamic environments due to moving objects.
- Dynamic objects disrupt epipolar geometry, leading to localization errors in robotic systems.
Purpose of the Study:
- To develop a novel method for enhancing visual SLAM accuracy in outdoor dynamic environments.
- To address the problem of reduced localization precision caused by dynamic objects.
Main Methods:
- Utilized YOLOv5s with an attention mechanism for initial detection of dynamic objects.
- Implemented an adaptive feature point selection system based on dynamic object presence and frame occupancy.
- Employed Lucas-Kanade optical flow and RANSAC for dynamic region determination.
Main Results:
- The proposed system demonstrated significant reductions in Absolute Trajectory Error (up to 39%) and Relative Trajectory Error (up to 30%) on the KITTI dataset.
- Outperformed various dynamic feature point selection strategies and DynaSLAM in accuracy evaluations.
Conclusions:
- The adaptive feature point selection system effectively mitigates the negative impact of dynamic objects on visual SLAM.
- This approach offers a robust solution for improving robotic localization in complex, dynamic outdoor settings.
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