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

2025-09-06

AI Summary

To address the real-time execution challenges of LiDAR-inertial odometry (LIO) on resource-constrained platforms—such as aerial robots—due to computational and memory bottlenecks, this paper proposes OctVox, a compact octree-based voxel map structure enabling density-adaptive point cloud representation and incremental denoising. We further introduce a spatial locality-aware heuristic K-nearest neighbors (HKNN) search strategy, integrated with sub-voxel fusion and a tightly coupled optimization framework, to significantly improve matching efficiency and robustness. Experimental evaluation across multiple platforms demonstrates that our method achieves approximately 73% higher frame processing speed compared to state-of-the-art approaches, reduces CPU utilization, maintains comparable pose estimation accuracy, and supports plug-and-play deployment with open-source implementation.

Technology Category

Intelligent Robots: State EstimationSearch and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSystems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and servicesSearch and Retrieval-Augmented AI: Vertical and domain-specific search

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

Problem

• Addresses computational and memory limitations in LiDAR-Inertial Odometry
• Enables efficient deployment on resource-constrained autonomous platforms
• Reduces runtime overhead while maintaining high accuracy performance

Innovation

• Compact octo-voxel map structure for density control
• Heuristic-guided KNN strategy for faster correspondence search
• Efficient and robust LiDAR-inertial odometry system