Abstract: Addressing the issues that visual SLAM has data association mismatch in dynamic scenarios and false detection in instance segmentation, an indoor dynamic point feature detection method based on improved instance segmentation is proposed. Firstly, the YOLOv7-seg algorithm is improved, and a double gradient path aggregation network (D-ELAN) and a hole attention mechanism (DwCBAM) are designed to obtain the accurate contour information of dynamic objects in the current image frame. Secondly, dynamic feature points are eliminated from the SLAM front-end image frames after determining the object class. Finally, static points are utilized to construct an error optimization model. The experimental results show that the improved algorithm increases the mAP by 2.3% on average compared to YOLOv7-seg. On the TUM dataset, the method reduces the SLAM absolute trajectory error by 95.91% on average compared to ORB-SLAM2.

Key words: visual SLAM, instance segmentation, dynamic reject, pose estimation