A robust visual SLAM system in dynamic man-made environments

Abstract

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 3D maplines after initialization. To cope with dynamic scenarios, the proposed system are divided into four main threads including 2D dynamic object tracking, visual odometry, local mapping and loop closing. The 2D 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 3D lines, we use Plucker 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.