Robust Perception-Based Visual Simultaneous Localization and Tracking in Dynamic Environments

Visual Simultaneous Localization and Mapping (SLAM) in dynamic scenes is a prerequisite for robot-related applications. Most of the existing SLAM algorithms mainly focus on dynamic object rejection, which makes part of the valuable information lost and prone to failure in complex environments. This paper proposes a semantic visual SLAM system that incorporates rigid object tracking. A robust scene perception frame is designed, which gives autonomous robots the ability to perceive scenes similar to human cognition. Specifically, we propose a two-stage mask revision method to generate fine mask of the object. Based on the revised mask, we propose a semantic and geometric constraint (SAG) strategy, which provides a fast and robust way to perceive dynamic rigid objects. Then, the motion tracking of rigid objects is integrated into the SLAM pipeline, and a novel bundle adjustment is constructed to optimize camera localization and object' 6-DoF poses. Finally, the evaluation of the proposed algorithm is performed on publicly available KITTI dataset, Oxford Multimotion Dataset, and real-world scenarios. The proposed algorithm achieves the comprehensive performance of RPE t less than 0.07m per frame and RPE R about 0.03° per frame in the KITTI dataset. The experimental results reveal that the proposed algorithm enables accurate localization and robust tracking than state-of-the-art SLAM algorithms in challenging dynamic scenarios.