Visual SLAM based on semantic information and geometric constraints in dynamic environment

Abstract

Most existing visual SLAM systems assume that the external environment is static, ignoring the influence of dynamic objects on the SLAM system. This assumption largely affects the accuracy and robustness of autonomous navigation. To address this issue, a dynamic SLAM system was proposed, which combined semantic information based on object detection and geometric information from multi-view geometry constraints by defining and discriminating the dynamic feature points in the system based on the moving probability. Experiment results on the public TUM dataset and our robot in real environment showed that, when comparing with ORB-SLAM2, the absolute trajectory error could be reduced larger than 94%, and the average relative position and attitude errors were reduced at least 41% and 40%, respectively, in high dynamic environments. It means that the proposed SLAM system effectively removes dynamic feature points, thus improving the localization accuracy and robustness of the visual SLAM system within high dynamic environments.

Keywords

• dynamic SLAM;deep learning;object detection;epipolar geometry