SLAM-X: Generalizable Dynamic Removal for NeRF and Gaussian Splatting SLAM

摘要

NeRF-SLAM and GS-SLAM demonstrate excellent performance in high-fidelity rendering and real-time reconstruction in static scenes. However, real-world environments are often filled with dynamic objects, leading to tracking errors and mapping failures. Several dynamic SLAM approaches have been proposed, but they remain difficult to adopt due to challenges in deployment, framework compatibility, and generalization. To address these challenges, we introduce SLAM-X, the first plug-and-play module designed to universally enhance dynamic scene handling across a range of SLAM architectures. SLAM-Xleverages zero-shot segmentation and adaptively tracked sparse optical flow to generate dynamic masks, enabling tracking and mapping correction through continuous scene learning, while removing dynamic artifacts without requiring any task-specific fine-tuning. Extensive experiments on multiple real-world datasets demonstrate that SLAM-X effectively mitigates dynamic disturbances and seamlessly integrates with various NeRF-SLAM and GS-SLAM frameworks, achieving state-of-the-art performance in dynamic environments.