A Lightweight and Robust LiDAR-Inertial Localization Algorithm Based on Sparse Maps

High-precision and robust autonomous localization is an essential technology for uncrewed systems to achieve reliable navigation. Traditional map-based positioning methods can provide high localization accuracy, but as the size of the environment increases, these methods often require significant computational resources and memory, making them unsuitable for resource-constrained platforms. To address this, we propose a lightweight localization method based on sparse maps, designed to reduce computational overhead while maintaining high precision and robustness. Our approach combines light detection and ranging (LiDAR) and inertial measurements by extracting planar features from local regions for scan matching. This method reduces data processing complexity, ensuring both real-time performance and localization accuracy. Unlike traditional methods that match LiDAR data with the global map for every frame, we perform global map matching only at specific intervals, applying pose corrections through a time threshold mechanism, thereby significantly reducing computational burden. Additionally, to improve the accuracy and stability of state estimation, this article integrates LiDAR data at specific time intervals with the results of map matching, along with planar feature matching results extracted from local regions, to achieve precise pose correction. Combined with a nonlinear geometric observer featuring strong convergence properties, this approach enhances localization accuracy and robustness through multisensor fusion. Experimental results on publicly available large-scale datasets and our self-collected datasets demonstrate that our method outperforms existing map-based localization methods by achieving the lowest state update time and reducing computational costs by over 50%, while maintaining robust performance in complex environments. Data will be publicly available on https://github.com/iron2523/sparse-map_localization