Optimized feature extraction and object detection for indoor dynamic environment visual SLAM study

Abstract

This study introduces the YORB-SLAM algorithm, a novel approach that integrates an enhanced ORB-SLAM2 framework with a lightweight YOLOv5 model to improve the robustness and accuracy of visual SLAM systems in indoor dynamic environments. By incorporating a variable threshold FAST corner detection algorithm, we optimize feature point extraction performance under unstable lighting conditions. An improved quadtree algorithm not only accelerates feature extraction but also retains richer image information. Further, we tailor a lightweight YOLOv5 model to our application scenario through self-training and devise a set of dynamic feature point elimination rules, significantly boosting performance in dynamic indoor scenes. Evaluations on six dynamic indoor sequences from the TUM dataset show that YORB-SLAM significantly outperforms the original ORB-SLAM2 in accuracy and exhibits better real-time capabilities than DS-SLAM and DynaSLAM.