LIVOX-CAM: Adaptive Coarse-to-Fine Visual-Assisted LiDAR Odometry for Solid-State LiDAR

The application of solid-state LiDAR is expanding across diverse scenarios. However, most existing methods rely on IMU data fusion to achieve stable performance. This paper presents LIVOX-CAM, a visual-assisted LiDAR odometry based on KISS-ICP, specifically tailored for small field-of-view (FoV) solid-state LiDAR. The system adopts a two-stage architecture comprising a front-end for data pre-processing and a back-end for coarse-to-fine iterative pose optimization. The system is designed to significantly broaden its application scenarios by incorporating a spatial adaptive module and visual assistance. Extensive experiments on public and private datasets show that, even without IMU input, the proposed method achieves robust and accurate performance in challenging scenes, including autonomous driving, degraded scenarios, unstructured environments, and aerial mapping, exhibiting strong competitiveness against state-of-the-art approaches. To encourage reproducibility, the code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/huashu996/LIVOX-CAM</uri>.