文献类型：期刊文献

英文题名：Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy

作者：Wang, Liansheng[1]; Zhang, Xinke[1]; Li, Chenhui[2]; He, Dongjiao[3]; Pan, Yihan[1]; Yi, Jianjun[1]

机构：[1] Department of Mechanical Engineering, East China University of Science and Technology, Shanghai, China; [2] Shanghai Artificial Intelligence Laboratory, Shanghai, China; [3] University of Hong Kong, Hong Kong

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250381102)

语种：英文

外文关键词：Antennas - Lithium compounds - Open systems - Real time systems - Robots

摘要：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 subvoxel representatives, 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 compatible with a wide range of LiDAR sensors and computing platforms. The implementation is available at: https://github.com/Liansheng-Wang/Super-LIO.git. Copyright ? 2025, The Authors. All rights reserved.