TKG-SLAM: Dynamic SLAM based on target tracking and multi-view geometry

The dynamic nature of the scene violates the static environment assumption of the SLAM algorithm, reducing the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues like bad realtime performance, the potential to mistakenly treat people as static objects, and poor performance in highly dynamic environments still persist. Based on object detection, Kalman filtering and multi-view geometry, this paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM). This algorithm takes into consideration both realtime performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art methods, our method performs optimally in some scenarios and has stronger realtime capabilities.