Robust Key-Frame Stereo Visual SLAM with low-threshold Point and Line Features

TL;DR

This paper presents a robust stereo visual SLAM system that improves efficiency and accuracy by using low-threshold features, spatial suppression, and a closed-loop keyframe strategy, outperforming existing methods in standard datasets.

Contribution

The paper introduces a novel SLAM approach that integrates low-threshold point and line features with spatial inhibition and a closed-loop keyframe mechanism for enhanced robustness and efficiency.

Findings

Outperforms state-of-the-art methods on KITTI and EuRoC datasets

Reduces computational time by efficient feature suppression

Improves tracking robustness in low-texture environments

Abstract

In this paper, we develop a robust, efficient visual SLAM system that utilizes spatial inhibition of low threshold, baseline lines, and closed-loop keyframe features. Using ORB-SLAM2, our methods include stereo matching, frame tracking, local bundle adjustment, and line and point global bundle adjustment. In particular, we contribute re-projection in line with the baseline. Fusing lines in the system consume colossal time, and we reduce the time from distributing points to utilizing spatial suppression of feature points. In addition, low threshold key points can be more effective in dealing with low textures. In order to overcome Tracking keyframe redundant problems, an efficient and robust closed-loop tracking key frame is proposed. The proposed SLAM has been extensively tested in KITTI and EuRoC datasets, demonstrating that the proposed system is superior to state-of-the-art methods in various scenarios.