SDT-SLAM: A Short-Term Dynamic Tracking SLAM Algorithm for Dynamic Scenes Based on RGB-D Sensors

Existing dynamic scene visual SLAM methods indiscriminately remove feature points on dynamic target bounding boxes when using object detection algorithms to detect prior targets, resulting in the incorrect removal of some valid static points and affecting the localization accuracy of the algorithm. This paper proposes a visual SDT-SLAM algorithm, which uses YOLOv11 to determine the prior target information of each frame of the image and designs a short-term dynamic object tracking algorithm to track targets and classify feature points in real time for different categories of bounding boxes. In the state estimation process, a depth difference estimation algorithm is used to recover some erroneously deleted static feature points, and an epipolar constraint algorithm is used to filter dynamic feature points from the scene in a fine-grained manner. This method precisely distinguishes the dynamic and static feature points in dynamic scenes. Compared with other SLAM systems, this system reasonably utilizes depth information to restore static feature points in dynamic scenes, and based on semantic information and epipolar constraints, it removes incorrect matching points, significantly preserving and fully utilizing the static feature points in dynamic scenes. This further improves the trajectory estimation accuracy of the SLAM algorithm in dynamic scenes. Without relying on computationally intensive segmentation methods, it achieves a new balance between real-time performance and accuracy. The SDT-SLAM algorithm was validated on the TUM and Bonn public datasets and in the real world. The results show that SDT-SLAM has advantages in speed and accuracy for localization in highly dynamic environments compared to other advanced algorithms.