Visual SLAM algorithm based on lightweight object detection network in dynamic environments

To tackle the issue of inaccurate position estimation in traditional vision SLAM systems when dealing with dynamic objects, we developed a dynamic vision SLAM system that leverages a lightweight target detection network. This system enhances localization accuracy and robustness by identifying and removing dynamic feature points within the environment. By replacing the YOLOv5s backbone with the lightweight PP-LCNet, we introduced the PPLCNet-YOLOv5s target detection network, which reduces network parameters by 44.72% and boosts operating speed by 39%. This improved network is integrated into the tracking thread of the ORB-SLAM3 system to filter out dynamic feature points. Experiments on the TUM dataset demonstrate that this approach significantly improves SLAM system performance in dynamic environments, resulting in reduced trajectory errors and enhanced position estimation accuracy, thereby effectively strengthening the system’s robustness.