Selective Point Sampling for LiDAR-Inertial Odometry

Liu, Changxiang ; Yu, Hongshan ; Chen, Xieyuanli ; Lu, Huimin ; Wang, Jingchuan ; Huang, Yi ; Civera, Javier (Universidad de Zaragoza)

Resumen: The accuracy of LiDAR-Inertial Odometry (LIO) is highly sensitive to low-quality LiDAR measurements, which can substantially degrade state estimation. In this paper we propose Selective Point Sampling (SPS), a lightweight strategy that enhances LIO performance by discarding as outliers LiDAR measurements that are statistically inconsistent with their local map neighborhood. Consistency is quantified using the Kullback–Leibler (KL) divergence between each measurement’s distribution and the averaged distributions of its neighboring map points, and only measurements below a KL threshold are retained to form residuals for state updates. SPS is implemented as a modular plug-in compatible with existing EKF-based LIO frameworks, requiring minimal integration effort. Extensive experiments on public datasets—NCLT, M2DGR, Botanic Garden, and Newer College—and additional custom-collected sequences demonstrate consistent localization accuracy gains across multiple baselines with negligible computational overhead.

Idioma: Inglés

DOI: 10.1109/LRA.2025.3623043

Año: 2025

Publicado en: IEEE Robotics and Automation Letters 10, 12 (2025), 12875-12882
ISSN: 2377-3766

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)