Enhancing visual SLAM in dynamic environments with improved YOLOv8

The presence of moving objects in real-world scenarios can lead to mismatches in visual odometry feature points, thereby affecting the accuracy of positioning and mapping by the SLAM system and reducing its robustness in practical applications. This paper introduces a visual SLAM algorithm that leverages the ORB-SLAM3 framework and deep learning techniques. Enhancements to the SLAM system’s tracking thread enable identifying and removing dynamic feature points, thus increasing its adaptability to dynamic environments. Concurrently, YOLOv8s, known for its minimal depth and feature map width in the YOLOv5 series, is selected as the object detection network, with VanillaNet, a lightweight network, replacing its backbone network. This combination effectively determines the mobility of objects within the environment. Consequently, we propose an enhanced algorithm based on YOLOv8s capable of performing both object detection and semantic segmentation to eliminate dynamic feature points precisely. Ultimately, the algorithm’s accuracy and real-time performance were assessed using indoor dynamic scene data from the TUM RGB datasets. In comparison to models lacking any strategic approach, test results on the TUM datasets reveal that the experimental outcomes are more favorable in dynamic environments.