164052 20251121161351.0 doi 10.1109/LRA.2025.3623043 sideral 146192 ART-2025-146192 eng Liu, Changxiang Selective Point Sampling for LiDAR-Inertial Odometry 2025 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. info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Yu, Hongshan Chen, Xieyuanli Lu, Huimin Wang, Jingchuan Huang, Yi Civera, Javier Universidad de Zaragoza (orcid)0000-0003-1368-1151 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 10, 12 (2025), 12875-12882 IEEE Robot. autom. let. IEEE Robotics and Automation Letters 2377-3766 1861353 http://zaguan.unizar.es/record/164052/files/texto_completo.pdf Versión publicada 3621203 http://zaguan.unizar.es/record/164052/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:164052 articulos driver 2025-11-21-14:25:33 ARTICLE