Abstract: This paper proposes a dynamic scene visual odometry method based on deep learning. The C3Ghost module is built using the lightweight Ghost module combined with the target detection network YOLOv5s, and the CA (coordinate attention mechanism) is introduced to improve the network detection speed while ensuring detection accuracy. It is combined with the motion consistency algorithm to eliminate dynamic feature points and only use static feature points for pose estimation. Experimental results show that compared with the traditional ORB SLAM3 (orient FAST and rotated BRIEF simultaneous localization and mapping 3) algorithm, the ATE (absolute trajectory error) and RPE (relative pose error) on the TUM (technical university of Munich) RGB-D (RGB depth) high dynamic data set has improved by more than 90% on average. Compared with the advanced SLAM algorithm, it is also relatively improved. Therefore, this algorithm effectively improves the stability and robustness of visual SLAM in dynamic environments.

Key words: visual odometry, object detection, attention mechanism, lightweight, motion consistency