A robust visual SLAM system in dynamic man-made environments

This paper presents a robust visual simultaneous localization and mapping(SLAM) system that leverages point and structural line features in dynamic man-made environments. Manhanttan world assumption is considered and the structural line features in such man-made environments provide rich geometric constraint, e.g., parallelism. Such a geometric constraint can be therefore used to rectify 3 D maplines after initialization. To cope with dynamic scenarios, the proposed system are divided into four main threads including 2 D dynamic object tracking, visual odometry, local mapping and loop closing. The 2 D tracker is responsible to track the object and capture the moving object in bounding boxes. In such a case, the dynamic background can be excluded and the outlier point and line features can be effectively removed. To parameterize 3 D lines, we use Pl ¨ucker line coordinates in initialization and projection processes, and utilize the orthonormal representation in unconstrained graph optimization process. The proposed system has been evaluated in both benchmark datasets and real-world scenarios, which reveals a more robust performance in most of the experiments compared with the existing state-of-the-art methods.