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YO-SLAM: A Robust Visual SLAM towards Dynamic Environments

Visual Simultaneous Localization and Mapping (vSLAM) have been the topic for recent years. It works well under the static environment, but the results under dynamic environments are not satisfactory. In this paper, we propose YO-SLAM that can handle dynamic objects well. YO-SLAM adds instance segmentation thread on ORB-SLAM2 and makes changes to tracking thread. YO-SLAM combines instance segmentation network, optical tracking and epipolar constraint to eliminate influences from dynamic objects, therefore can obtain the accurate camera trajectories. We experiment on TUM RGB-D dataset and have carried on the comparison of ORB-SLAM2 and several dynamic systems. According to the experimental results, the system performance enhanced greatly compared to ORB-SLAM2, and is equally matched with other dynamic system performance.