Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM

This paper presents the first photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geometric accuracy, and real-time performance. The proposed method performs robust and accurate pose estimation within a continuous-time trajectory optimization framework, while incrementally reconstructing a 3D Gaussian map using camera and LiDAR data, all in real time. To effectively address under-reconstruction in regions not covered by the LiDAR, we employ a lightweight zero-shot depth model that synergistically combines RGB appearance cues with sparse LiDAR measurements to generate dense depth maps, enabling reliable Gaussian initialization in LiDAR-blind areas. We also explore how the incrementally reconstructed Gaussian map can improve the robustness of odometry by tightly incorporating photometric constraints from the Gaussian map into the continuous-time factor graph optimization.

arXiv preprint arXiv:2507.04004