FND-SLAM: A SLAM System Using Feature Points and NeRF in Dynamic Environments Based on RGB-D Sensors

Neural implicit visual simultaneous localization and mapping (SLAM) systems demonstrate high-fidelity mapping and robust tracking performance in static environments, making them a crucial solution for real-time dense reconstruction. However, in the real world, dynamic objects often interfere with the map construction and localization accuracy of the SLAM system, resulting in significant drift and artifacts. To address these issues, this article combines the strengths of traditional semantic SLAM and neural implicit SLAM by utilizing semantic segmentation and conditional random fields (CRFs) to detect and segment dynamic and static objects, preserving static points while evaluating the dynamic probability of pixels. We propose a keyframe selection and background inpainting strategy that significantly reduces reconstruction artifacts and enhances tracking robustness. Additionally, we introduce a depth penalty based on pixel dynamic probability to control rendering loss and mitigate the impact of transient interference. Our method was evaluated on the TUM RGB-D and Bonn datasets with dynamic interference and compared against existing traditional dynamic semantic SLAM and neural implicit SLAM systems. Experimental results show that our SLAM system maintains robust tracking and produces high-quality reconstructions, achieving a 90% reduction in average trajectory error (ATE) compared to state-of-the-art neural implicit SLAM systems.