VG-Mapping: Variation-Aware 3D Gaussians for Online Semi-static Scene Mapping

We propose VG-Mapping, an RGB-D online 3DGS mapping system tailored to semi-static scenes. To address this issue, (b) VG-Mapping introduces a variation-aware mapping mechanism that (c) efficiently and accurately updates the changed areas. Abstract--Maintaining an up-to-date map that accurately reflects recent changes in the environment is crucial, especially for robots that repeatedly traverse the same space. Failing to promptly update the changed regions can degrade map quality, resulting in poor localization, inefficient operations, and even lost robots. In this paper, we propose VG-Mapping, a novel online 3DGS-based mapping system tailored for such semi-static scenes. Our approach introduces a hybrid representation that augments 3DGS with a TSDF-based voxel map to efficiently identify changed regions in a scene, along with a variation-aware density control strategy that inserts or deletes Gaussian primitives in regions undergoing change. Furthermore, to address the absence of public benchmarks for this task, we construct a RGB-D dataset comprising both synthetic and real-world semi-static environments. Experimental results demonstrate that our method substantially improves the rendering quality and map update efficiency in semi-static scenes. IMUL T ANEOUS Localization and Mapping (SLAM) systems are widely applied in robotics, AR/VR.