Dynamic SLAM with Real-Time Semantic Segmentation and Inter-Frame and Multi-Frame Motion Feature Detection

Traditional Simultaneous Localization and Mapping (SLAM) methods often assume static environments. This limitation can lead to inaccurate localization or even the loss of tracking in dynamic scenes. To address this issue, we propose a novel SLAM approach specifically designed for dynamic environments. Our method integrates the real-time image semantic segmentation network BisenetV2 with inter-frame and continuous multi-frame motion feature detection. Firstly, semantic segmentation is applied to render the semantic mask, which is then used by the inter-frame motion detection module to identify potential motion features. Subsequently, these suspected motion features are evaluated by a likelihood probability model across consecutive frames. Finally, points with a high probability of motion are monitored in real-time by the Luenberger observer, which filters out motion features and re-adds static ones. Our experiments demonstrate that semantic segmentation can meet real-time requirements in various scenarios. Evaluations of the TUM-RGBD dataset and simulation experiment show that our proposed method significantly improves SLAM accuracy and robustness in dynamic scenes.