LHMM: A Tightly-Coupled LiDAR-Inertial Hybrid-Map Matching Approach for Robust and Efficient Global Localization

Abstract

LiDAR map matching (LMM) faces two key challenges: the enormous number of point clouds imposes constraints on storage and computation, and traditional two-stage frameworks suffer from initial guess errors during degeneration. This paper presents LHMM, a hybrid-map framework that first compresses the prior map and then performs tightly coupled pose estimation within a Maximum A Posteriori (MAP) estimation formulation. First, a skeletonization-based prior map compression method is proposed, which retains only stable structural features, reducing the map storage while enabling fast runtime association through a dual-mode map representation. Second, constraints from IMU, skeleton-feature prior map, and local voxel map are jointly optimized within a unified MAP formulation, recovering the full system state in a single step and preventing error cascades. The local map benefits from a hole-aware keyframe mechanism, focusing on regions with environmental changes or areas with partial map coverage, thereby reducing computation compared to full mapping. Extensive evaluations across multiple datasets demonstrate that LHMM not only reduces storage and computational overhead but also outperforms state-of-the-art methods in terms of localization accuracy and robustness. We will open-source the code<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.