VSS-SLAM: Voxelized Surfel Splatting for Geometally Accurate SLAM

Abstract

[1] Visual Simultaneous Localization and Mapping (SLAM) helps robots estimate their poses and perceive the environment in unknown settings. Recent work has demonstrated that implicit neural radiance fields and 3D Gaussian Splatting (3DGS) offer higher fidelity scene representation than traditional map representations. We propose VSS-SLAM, which utilizes voxelized surfels as the map representation for incremental mapping in unknown environments. This representation effectively addresses the issue of redundant and disordered primitives encountered in previous methods, thereby enhancing geometric accuracy during reconstruction. Specifically, our approach divides the scene using voxels and stores geometric and appearance information in feature vectors at the voxel vertices. Before rendering, these feature vectors are decoded to generate the corresponding surfels. Additionally, we align camera poses through image and depth rendering. Extensive experiments on the Replica and TUM-RGBD datasets demonstrate that VSS-SLAM delivers high-fidelity reconstruction and accurate pose estimation in both simulated and real-world environments. Source code will soon be available.