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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

HV-LIOM: Adaptive Hash-Voxel LiDAR–Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration

by
Shicheng Fan
1,
Xiaopeng Chen
1,*,
Weimin Zhang
1,2,
Peng Xu
1,
Zhengqing Zuo
1,
Xinyan Tan
1,
Xiaohai He
1,
Chandan Sheikder
1,
Meijun Guo
1 and
Chengxiang Li
1
1
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
2
Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou 450000, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(24), 7558; https://doi.org/10.3390/s25247558
Submission received: 29 October 2025 / Revised: 9 December 2025 / Accepted: 9 December 2025 / Published: 12 December 2025
(This article belongs to the Section Radar Sensors)
Versions Notes

Abstract

This paper presents HV-LIOM (Adaptive Hash-Voxel LiDAR–Inertial Odometry and Mapping), a unified LiDAR–inertial SLAM and autonomous exploration framework for real-time 3D mapping in dynamic, GNSS-denied environments. We propose an adaptive hash-voxel mapping scheme that improves memory efficiency and real-time state estimation by subdividing voxels according to local geometric complexity and point density. To enhance robustness to poor initialization, we introduce a multi-resolution relocalization strategy that enables reliable localization against a prior map under large initial pose errors. A learning-based loop-closure module further detects revisited places and injects global constraints, while global pose-graph optimization maintains long-term map consistency. For autonomous exploration, we integrate a Soft Actor–Critic (SAC) policy that selects informative navigation targets online, improving exploration efficiency in unknown scenes. We evaluate HV-LIOM on public datasets (Hilti and NCLT) and a custom mobile robot platform. Results show that HV-LIOM improves absolute pose accuracy by up to 15.2% over FAST-LIO2 in indoor settings and by 7.6% in large-scale outdoor scenarios. The learned exploration policy achieves comparable or superior area coverage with reduced travel distance and exploration time relative to sampling-based and learning-based baselines.
Keywords: LiDAR-based SLAM; Hybrid Voxel Mapping; active exploration; reinforcement learning; autonomous exploration LiDAR-based SLAM; Hybrid Voxel Mapping; active exploration; reinforcement learning; autonomous exploration

Share and Cite

MDPI and ACS Style

Fan, S.; Chen, X.; Zhang, W.; Xu, P.; Zuo, Z.; Tan, X.; He, X.; Sheikder, C.; Guo, M.; Li, C. HV-LIOM: Adaptive Hash-Voxel LiDAR–Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration. Sensors 2025, 25, 7558. https://doi.org/10.3390/s25247558

AMA Style

Fan S, Chen X, Zhang W, Xu P, Zuo Z, Tan X, He X, Sheikder C, Guo M, Li C. HV-LIOM: Adaptive Hash-Voxel LiDAR–Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration. Sensors. 2025; 25(24):7558. https://doi.org/10.3390/s25247558

Chicago/Turabian Style

Fan, Shicheng, Xiaopeng Chen, Weimin Zhang, Peng Xu, Zhengqing Zuo, Xinyan Tan, Xiaohai He, Chandan Sheikder, Meijun Guo, and Chengxiang Li. 2025. "HV-LIOM: Adaptive Hash-Voxel LiDAR–Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration" Sensors 25, no. 24: 7558. https://doi.org/10.3390/s25247558

APA Style

Fan, S., Chen, X., Zhang, W., Xu, P., Zuo, Z., Tan, X., He, X., Sheikder, C., Guo, M., & Li, C. (2025). HV-LIOM: Adaptive Hash-Voxel LiDAR–Inertial SLAM with Multi-Resolution Relocalization and Reinforcement Learning for Autonomous Exploration. Sensors, 25(24), 7558. https://doi.org/10.3390/s25247558

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