Multi-LIO: A Lightweight Multiple LiDAR-Inertial Odometry System

Abstract

The integration of multiple LiDAR sensors has the potential to significantly enhance odometry systems by providing comprehensive environmental measurements. However, current multiple LiDAR-inertial odometry frameworks face challenges in real-time processing due to the voluminous data generated. This paper introduces a real-time, computationally efficient multiple LiDAR-inertial odometry system (Multi-LIO) that outperforms existing state-of-the-art solutions in accuracy and scalability. Utilizing a novel parallel strategy for state updates and a voxelized map format, Multi-LIO optimizes computational efficiency. Furthermore, we introduce a point-wise uncertainty estimation method to augment the accuracy of scan-to-map registration, particularly in large-scale and complex scenarios. We validate our system’s performance through extensive experiments on various challenging sequences. Multi-LIO emerges as a robust, scalable, and extensible solution, adaptable to various LiDAR configurations.