NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments
This addresses the challenge of robust visual SLAM in dynamic scenes for robotics and AR/VR applications, representing an incremental improvement over existing neural SLAM methods.
The paper tackled the problem of neural SLAM struggling with moving objects in dynamic environments, and the result was NID-SLAM, which improved tracking accuracy and mapping quality by enhancing semantic masks and using a keyframe selection strategy, outperforming competitive methods on RGB-D datasets.
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.