超LIO：一种鲁棒且高效的激光雷达-惯性里程计系统，具有紧凑的映射策略
Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
AI总结：
Super-LIO通过紧凑的OctVox映射结构和启发式引导的KNN策略，实现了高效的鲁棒LIO系统，显著提升了处理速度和资源利用率。
摘要：
激光雷达-惯性里程计（LIO）是自动驾驶系统的基础技术，但其在资源受限平台上部署仍具挑战性，因计算和内存限制。我们提出了Super-LIO，一种要求高性能和准确性的鲁棒LIO系统，适用于无人机和移动自主系统。Super-LIO的核心是一种紧凑的八元体素映射结构，称为OctVox，每个体素限制为八个融合的子体素，使点密度控制严格，并在地图更新期间实现增量去噪。这种设计使映射结构简单且高效且准确，可轻松集成到现有LIO框架中。此外，Super-LIO设计了一种启发式引导的KNN策略（HKNN），通过利用空间局部性加速对应搜索，进一步减少运行时间开销。我们使用四个公开可用的数据集和多个自采集的数据集评估了所提出的系统，总计超过30个序列。在X86和ARM平台上的广泛测试证实，Super-LIO在效率和鲁棒性方面表现优异，同时保持竞争性的准确性。Super-LIO处理每个帧的速度比SOTA快约73%，同时消耗更少的CPU资源。该系统完全开源，并且与广泛激光雷达传感器和平台兼容。实现可在：https://github.com/Liansheng-Wang/Super-LIO.git上获得。
英文摘要：
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 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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
