GMM-LIO: Adaptive and Robust LiDAR-Inertial Odometry with Gaussian Mixture Model Voxel Map

Abstract

Tightly coupled LiDAR–inertial odometry (LIO) systems are critical for autonomous navigation, yet their performance often degrades due to insufficient adaptability to diverse environments and limitations in map representation. To address these limitations, this paper presents GMM-LIO, a robust and adaptive LIO framework that integrates a novel information-theoretic scan processing module and a high-fidelity Gaussian Mixture Model (GMM) voxel map structure. At its core, GMM-LIO features a two-level adaptive front-end that dynamically modulates voxel resolution based on state uncertainty and adjusts surface covariance estimation according to local point density on a standard voxel grid. Furthermore, GMM-LIO employs a dynamic Gaussian Mixture Model voxel map to accurately model intersecting surfaces. The entire system is formulated as a robust Maximum a Posteriori (MAP)-based estimator, which employs an Iteratively Reweighted Least Squares (IRLS) solver together with a principled anisotropic information matrix to handle measurement outliers. Extensive evaluations on diverse public and self-collected datasets demonstrate that GMM-LIO achieves state-of-the-art accuracy and robustness, with a 36% relative improvement over leading LIO baselines.