An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments

Recent developments in robotics have heightened the need for visual SLAM. Dynamic objects are a major problem in visual SLAM which reduces the accuracy of localization due to the wrong epipolar geometry. This study set out to find a new method to address the low accuracy of visual SLAM in outdoor dynamic environments. We propose an adaptive feature point selection system for outdoor dynamic environments. Initially, we utilize YOLOv5s with the attention mechanism to obtain a priori dynamic objects in the scene. Then, feature points are selected using an adaptive feature point selector based on the number of a priori dynamic objects and the percentage of a priori dynamic objects occupied in the frame. Finally, dynamic regions are determined using a geometric method based on Lucas-Kanade optical flow and the RANSAC algorithm. We evaluate the accuracy of our system using the KITTI dataset, comparing it to various dynamic feature point selection strategies and DynaSLAM. Experiments show that our proposed system demonstrates a reduction in both absolute trajectory error and relative trajectory error, with a maximum reduction of 39% and 30%, respectively, compared to other systems.

Automated summary

Recent developments in robotics have led to an increased demand for visual SLAM, however, dynamic objects pose a major problem reducing localization accuracy. This study proposes an adaptive feature point selection system for outdoor dynamic environments to address this issue. The system uses YOLOv5s to identify dynamic objects, selects feature points based on their presence and occupancy in the frame, and determines dynamic regions using Lucas-Kanade optical flow and RANSAC algorithm. Evaluation on the KITTI dataset shows a significant reduction in absolute and relative trajectory errors compared to other systems.