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.

提出了一种名为 NID-SLAM 的方法，通过增强语义掩膜中的不准确区域和选择关键帧来提高神经 SLAM 在动态环境中的性能。在动态环境中，该方法通过准确删除动态物体来降低相机漂移的概率，从而在跟踪准确性和建图质量方面优于其他竞争性神经 SLAM 方法。