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

Abstract

This paper presents a 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 large-scale 3D Gaussian map using camera and LiDAR data, all in real time. The resulting map enables high-quality, real-time novel view rendering of both RGB images and depth maps. 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. The depth completion enables reliable Gaussian initialization in LiDAR-blind areas, significantly improving system applicability for sparse LiDAR sensors. To enhance geometric accuracy, we fully use sparse but precise LiDAR depths to supervise Gaussian map optimization and accelerate it with carefully designed CUDA-accelerated strategies. Furthermore, we explore how the incrementally reconstructed Gaussian map can improve the robustness of the odometry. By efficiently incorporating photometric constraints from the Gaussian map into the continuous-time factor graph optimization, we demonstrate improved tracking performance, especially under LiDAR degenerated scenarios. We also showcase downstream applications via extending our elaborate system, including video frame interpolation and fast 3D mesh production. To support rigorous evaluation, we collect a dedicated LiDAR-Inertial-Camera dataset featuring ground-truth poses, depth maps, and extrapolated trajectories for assessing out-of-sequence novel view synthesis in large-scale outdoor scenarios. Extensive experiments on both public and self-collected datasets demonstrate the superiority and versatility of our system across LiDAR sensors with varying sampling densities.

The authors' abstract, as published at the source. The International Journal of Robotics Research, 2026 · DOI ↗