{"ID":2873976,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05723","arxiv_id":"2509.05723","title":"Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy","abstract":"LiDAR-Inertial Odometry (LIO) is a foundational technique for autonomous systems, yet its deployment on resource-constrained platforms remains challenging due to computational and memory limitations. We propose Super-LIO, a robust LIO system that demands both high performance and accuracy, ideal for applications such as aerial robots and mobile autonomous systems. At the core of Super-LIO is a compact octo-voxel-based map structure, termed OctVox, that limits each voxel to eight fused subvoxels, enabling strict point density control and incremental denoising during map updates. This design enables a simple yet efficient and accurate map structure, which can be easily integrated into existing LIO frameworks. Additionally, Super-LIO designs a heuristic-guided KNN strategy (HKNN) that accelerates the correspondence search by leveraging spatial locality, further reducing runtime overhead. We evaluated the proposed system using four publicly available datasets and several self-collected datasets, totaling more than 30 sequences. Extensive testing on both X86 and ARM platforms confirms that Super-LIO offers superior efficiency and robustness, while maintaining competitive accuracy. Super-LIO processes each frame approximately 73% faster than SOTA, while consuming less CPU resources. The system is fully open-source and plug-and-play compatible with a wide range of LiDAR sensors and platforms. The implementation is available at: https://github.com/Liansheng-Wang/Super-LIO.git","short_abstract":"LiDAR-Inertial Odometry (LIO) is a foundational technique for autonomous systems, yet its deployment on resource-constrained platforms remains challenging due to computational and memory limitations. We propose Super-LIO, a robust LIO system that demands both high performance and accuracy, ideal for applications such a...","url_abs":"https://arxiv.org/abs/2509.05723","url_pdf":"https://arxiv.org/pdf/2509.05723v2","authors":"[\"Liansheng Wang\",\"Xinke Zhang\",\"Chenhui Li\",\"Dongjiao He\",\"Yihan Pan\",\"Jianjun Yi\"]","published":"2025-09-06T14:07:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":610105,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2873976,"paper_url":"https://arxiv.org/abs/2509.05723","paper_title":"Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy","repo_url":"https://github.com/Liansheng-Wang/Super-LIO.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}