关键词: 视觉SLAM, YOLO目标识别, 空间定位, 室内动态场景

Abstract: 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 categorized based on their motion properties， and object detection is performed using the YOLOv5s neural network to obtain the positions of the target detection boxes for subsequent dynamic feature point screening. Then， a feature point selection strategy is designed， 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 reduced compared to ORB-SLAM2， validating the effectiveness of the proposed method.

Key words: visual SLAM, YOLO object detection, spatial positioning, indoor dynamic scenarios