GaussR-SLAM: Gaussian Robust SLAM in Data Loss and Interference Environments

Abstract

Recent advancements in 3DGS-based explicit mapping have significantly improved SLAM performance, achieving more realistic environment reconstruction and faster processing. However, issues such as data loss caused by unstable data transmission, textureless and repetitive-texture often occur in real-world scenarios. These sensor degradation problems lead to tracking drift caused by incorrect feature or pixel matching, as well as artifacts due to rendering errors. To address these challenges, we propose the GaussR-SLAM, the first 3DGS-based SLAM system designed for sensor degradation scenarios. By initializing Gaussians using hybrid feature points and employing an adaptive tracking switch mechanism, we achieve efficient data association and pose correction. In the mapping thread, we propose fusion pruning based on the spatial distribution of Gaussians to eliminate artifacts and mapping errors, while also designing a hybrid descriptor loss for rendered images to achieve photorealistic rendering. Experimental results on standard datasets demonstrate that our system outperforms existing 3DGS-based SLAM systems under sensor degradation, particularly in scenarios involving data loss.