VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels

Accurate and robust pose estimation is critical for simultaneous localization and mapping (SLAM), and multi-sensor fusion has demonstrated efficacy with significant potential for robotic applications. This study presents VOX-LIO, an effective LiDAR-inertial odometry system. To improve both robustness and accuracy, we propose an adaptive hash voxel-based point cloud map management method that incorporates surfel features and planarity. This method enhances the efficiency of point-to-surfel association by leveraging long-term observed surfel. It facilitates the incremental refinement of surfel features within classified surfel voxels, thereby enabling precise and efficient map updates. Furthermore, we develop a weighted fusion approach that integrates LiDAR and IMU data measurements on the manifold, effectively compensating for motion distortion, particularly under high-speed LiDAR motion. We validate our system through experiments conducted on both public datasets and our mobile robot platforms. The results demonstrate that VOX-LIO outperforms the existing methods, effectively handling challenging environments while minimizing computational cost.