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.

翻译：神经隐式表示已被探索用于增强视觉SLAM算法，特别是在提供高保真稠密地图方面。现有方法在静态场景中运行稳健，但难以应对运动物体造成的干扰。本文提出NID-SLAM，该方法显著提升了神经SLAM在动态环境中的性能。我们提出了一种新方法来增强语义掩膜中的不准确区域，特别是边缘区域。利用深度图像中的几何信息，该方法能够精确移除动态物体，从而降低相机漂移的概率。此外，我们引入了一种针对动态场景的关键帧选择策略，该策略增强了大尺度物体下相机跟踪的鲁棒性，并提升了建图效率。在公开可用的RGB-D数据集上的实验表明，我们的方法在动态环境中的跟踪精度和建图质量上优于具有竞争力的神经SLAM方法。