  Neural implicit representations have been explored to enhance visual SLAM
algorithms, especially in providing high-fidelity dense map. Existing methods
operate robustly in static scenes but struggle with the disruption caused by
moving objects. In this paper we present NID-SLAM, which significantly improves
the performance of neural SLAM in dynamic environments. We propose a new
approach to enhance inaccurate regions in semantic masks, particularly in
marginal areas. Utilizing the geometric information present in depth images,
this method enables accurate removal of dynamic objects, thereby reducing the
probability of camera drift. Additionally, we introduce a keyframe selection
strategy for dynamic scenes, which enhances camera tracking robustness against
large-scale objects and improves the efficiency of mapping. Experiments on
publicly available RGB-D datasets demonstrate that our method outperforms
competitive neural SLAM approaches in tracking accuracy and mapping quality in
dynamic environments.
