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Neural implicit representations have been explored to enhance visual SLAMalgorithms, especially in providing high-fidelity dense map. Existing methodsoperate robustly in static scenes but struggle with the disruption caused bymoving objects. In this paper we present NID-SLAM, which significantly improvesthe performance of neural SLAM in dynamic environments. We propose a newapproach to enhance inaccurate regions in semantic masks, particularly inmarginal areas. Utilizing the geometric information present in depth images,this method enables accurate removal of dynamic objects, thereby reducing theprobability of camera drift. Additionally, we introduce a keyframe selectionstrategy for dynamic scenes, which enhances camera tracking robustness againstlarge-scale objects and improves the efficiency of mapping. Experiments onpublicly available RGB-D datasets demonstrate that our method outperformscompetitive neural SLAM approaches in tracking accuracy and mapping quality indynamic environments.

NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments