Research on Dynamic Scene SLAM Based on Improved Object Detection
Aiming at the epipolar constraint matching problem of monocular SLAM in dynamic scenes a dynamic feature point selection method based on object detection is proposed,in which the dynamic feature points in the front-end image frame of SLAM system is eliminated during feature extraction to improve the localization accuracy of SLAM.An improved target detection network is proposed to construct a loss function to describe the bounding box by using the overlap area,distance similarity and cosine similarity,which can achieve the accurate localization of target objects and obtain the range of object feature points in the current image frame.The object category is judged in SLAM,and the dynamic feature points in the front-end image frame are rejected according to the target detection result for the objects marked as dynamic.Based on the static feature point results,the epipolar geometry is used for the feature matching between two frames to estimate pose the to carry out the tracking,map building and closed-loop detection of monocular camera motion.The speed of the inference process is improved by the structural reparameterization of the backbone of target detection network to ensure the real-time operation of the overall system.Experimental results on KITTI dataset show that the improved system improves the localization accuracy by 23.4%over ORB-SLAM3 system,and the frame rate can reach more than 30fps.The algorithm can effectively improve the localization accuracy of monocular SLAM system in dynamic scenes under the condition of ensuring the real-time operation.
visual SLAMepipolar geometryfeature matchingobject detectionIoU loss functionstructural reparameterization
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