SLM-SLAM: a visual SLAM system based on segmented large-scale model in dynamic scenes and zero-shot conditions

Abstract

In practical applications, the existence of diverse dynamic objects can compromise the localization precision of most conventional Visual Simultaneous Localization and Mapping (VSLAM) systems. Simultaneously, many dynamic VSLAM systems based on neural networks require pre-training for specific application scenarios. We introduce SLM-SLAM, the first VSLAM system that implements zero-shot processing of dynamic scenes. It achieves the capability to handle various dynamic objects without the necessity for pre-training, enabling straightforward adaptation to different application scenarios. Firstly, we designed an open-world semantic segmentation module based on a segmented large-scale model to acquire semantic information in the scene. Subsequently, we devised a label-based strategy for selecting feature points, jointly optimizing poses with the weighted labels provided by both semantic and geometric information. Finally, we refined the keyframe selection strategy of ORB-SLAM3 to prevent matching errors caused by an insufficient number of remaining static feature points in the scene. We conducted experiments on the TUM dataset, the KITTI dataset, and real-world scenarios. The results indicate that in dynamic scenes, our SLM-SLAM significantly improves localization accuracy compared to ORB-SLAM3, and its performance is comparable to state-of-the-art dynamic VSLAM systems.