YOLACT network-based algorithm of visual SLAM of mobile robots
A visual SLAM algorithm for indoor dynamic scenes was proposed.The instance segmentation net-work YOLACT was introduced to eliminate most of the dynamic points.The multi-view geometry was used to further filter the dynamic feature points that were not eliminated outside the segmentation mask.The remaining static feature points were used as camera pose estimation.At the same time,the point cloud map was construc-ted,the octree map was transformed and established;background repair was used to restore the background after dynamic objects were removed.Finally,in order to verify the effectiveness of the proposed algorithm,the TUM dataset was used for testing,and compared with the ORB-SLAM2 algorithm and other SLAM algorithms processing dynamic scenarios,and results show that the proposed algorithm performs well on the highly dyna-mic dataset.Compared with the ORB-SLAM2 algorithm,the positioning accuracy of the proposed algorithm in indoor dynamic scenes is improved by 93.06%,and it can be applied to the later use of robot positioning and navigation.
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