USP-SLAM: Deep Learning Based Visual SLAM with Robust Feature Extraction under Dynamic Environments.

The majority of visual SLAM systems that rely on feature point methods assume that scenes are static. Consequently, addressing the interference caused by dynamic objects in reality poses a persistent challenge. This paper proposes a visual semantic SLAM system, USP-SLAM, to address this challenge. It takes advantage of a lightweight feature extraction network, modified based on the Superpoint network, to realize robust feature extraction even with images of low illumination and rapid viewpoint changes. Moreover, a modified version of the Unet network is incorporated into USP-SLAM to achieve accurate semantic segmentation of dynamic objects. Combining these two networks, the dynamic point removal module within USP-SLAM uses the optical flow method to achieve satisfying visual SLAM performance. The experimental results on the publicly available TUM RGB-D dataset show that for dynamic scene sequences, USP-SLAM improves the absolute trajectory error by 96.12% compared to its reference system ORBSLAM2. Besides, USP-SLAM also outperforms in non-dynamic scenarios, demonstrating the superiority of its system design.