WF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features

Abstract

The assumption of a static environment is typical in many visual simultaneous localization and mapping (VSLAM) systems. However dynamic objects in open scenes will mislead feature association and even fail to match, which reduces the accuracy of localization. For dynamic scenarios, a robust visual SLAM system that utilizes weighted features, namely, named WF-SLAM is proposed in this paper, which is based on ORB-SLAM2. First, WF-SLAM applies the tightly coupled semantic and geometric dynamic target detection algorithm to obtain the dynamic information in the scene. Then, WF-SLAM defines feature point weights and initializes them with the dynamic information. Finally, the pose optimization in ORB-SLAM2 is changed to weight-based joint optimization. WF-SLAM significantly decreases mismatch and improves the accuracy of localization. Experiments are performed on the benchmark RGB-D dataset TUM and real-world scenarios, and the results demonstrate that WF-SLAM realizes significant improvements in term of localization accuracy compared to ORB-SLAM2 in dynamic environments and a more robust performance compared with state-of-the-art dynamic SLAM methods.