In response to the low spatial positioning accuracy and insufficient robustness in indoor dynamic scenarios,this paper proposes an enhanced positioning method suitable for indoor dynamic environments.Firstly,common indoor objects are catego-rized based on their motion properties,and object detection is performed using the YOLOv5s neural network to obtain the posi-tions of the target detection boxes for subsequent dynamic feature point screening.Then,a feature point selection strategy is de-signed,which uses edge detection and depth information filtering to determine which feature points within the target detection boxes have the potential for dynamic motion.Finally,a keyframe selection algorithm that integrates time step and the number of feature points is proposed to eliminate redundant keyframes and reduce feature information overlap between multiple frames.The proposed positioning enhancement method is transplanted into ORB-SLAM2 and tested based on the publicly available RGB-D dataset from the Technical University of Munich(TUM).The experimental results show that the average positioning error has re-duced compared to ORB-SLAM2,validating the effectiveness of the proposed method.
维普
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