A Lightweight Deep-Learning Visual SLAM for Indoor Dynamic Environment Using Yolov10

Abstract

Vision simultaneous localization and mapping (SLAM) technology has become a key research direction in the field of mobile robotics in recent years. However, the accuracy and stability of traditional vision SLAM technology greatly affected by the dynamic environment, and the mainstream dynamic feature point rejection method combining vision and semantic segmentation techniques is not applicable to edge-end devices with limited resources and high real-time requirements. This research suggests a visual SLAM algorithm based on the YOLOv10n lightweight target detection model and the GCNv2 feature point extraction model to accomplish real-time dynamic feature point rejection to address those problems. To compensate for the detection accuracy and stability issues of the YOLOv10n model while leveraging its real-time advantages, the algorithm also employs multi-target Kalman filtering with data association through the Hungarian algorithm, history window smoothing, and potential dynamic feature point recording methods to enhance robustness. The algorithm is validated on the TUM RGB-D Dataset, and the results demonstrate that the method can effectively reject the dynamic feature points in the dynamic environment, and it has a significant improvement in the accuracy and stability of the visual SLAM system.