Adaptive Motorized LiDAR Scanning Control for Robust Localization with OpenStreetMap
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909788515860480 |
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| author | Li, Jianping Zhu, Kaisong Liu, Zhongyuan Jin, Rui Xu, Xinhang Wan, Pengfei Xie, Lihua |
| author_facet | Li, Jianping Zhu, Kaisong Liu, Zhongyuan Jin, Rui Xu, Xinhang Wan, Pengfei Xie, Lihua |
| contents | LiDAR-to-OpenStreetMap (OSM) localization has gained increasing attention, as OSM provides lightweight global priors such as building footprints. These priors enhance global consistency for robot navigation, but OSM is often incomplete or outdated, limiting its reliability in real-world deployment. Meanwhile, LiDAR itself suffers from a limited field of view (FoV), where motorized rotation is commonly used to achieve panoramic coverage. Existing motorized LiDAR systems, however, typically employ constant-speed scanning that disregards both scene structure and map priors, leading to wasted effort in feature-sparse regions and degraded localization accuracy. To address these challenges, we propose Adaptive LiDAR Scanning with OSM guidance, a framework that integrates global priors with local observability prediction to improve localization robustness. Specifically, we augment uncertainty-aware model predictive control with an OSM-aware term that adaptively allocates scanning effort according to both scene-dependent observability and the spatial distribution of OSM features. The method is implemented in ROS with a motorized LiDAR odometry backend and evaluated in both simulation and real-world experiments. Results on campus roads, indoor corridors, and urban environments demonstrate significant reductions in trajectory error compared to constant-speed baselines, while maintaining scan completeness. These findings highlight the potential of coupling open-source maps with adaptive LiDAR scanning to achieve robust and efficient localization in complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11742 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Motorized LiDAR Scanning Control for Robust Localization with OpenStreetMap Li, Jianping Zhu, Kaisong Liu, Zhongyuan Jin, Rui Xu, Xinhang Wan, Pengfei Xie, Lihua Robotics LiDAR-to-OpenStreetMap (OSM) localization has gained increasing attention, as OSM provides lightweight global priors such as building footprints. These priors enhance global consistency for robot navigation, but OSM is often incomplete or outdated, limiting its reliability in real-world deployment. Meanwhile, LiDAR itself suffers from a limited field of view (FoV), where motorized rotation is commonly used to achieve panoramic coverage. Existing motorized LiDAR systems, however, typically employ constant-speed scanning that disregards both scene structure and map priors, leading to wasted effort in feature-sparse regions and degraded localization accuracy. To address these challenges, we propose Adaptive LiDAR Scanning with OSM guidance, a framework that integrates global priors with local observability prediction to improve localization robustness. Specifically, we augment uncertainty-aware model predictive control with an OSM-aware term that adaptively allocates scanning effort according to both scene-dependent observability and the spatial distribution of OSM features. The method is implemented in ROS with a motorized LiDAR odometry backend and evaluated in both simulation and real-world experiments. Results on campus roads, indoor corridors, and urban environments demonstrate significant reductions in trajectory error compared to constant-speed baselines, while maintaining scan completeness. These findings highlight the potential of coupling open-source maps with adaptive LiDAR scanning to achieve robust and efficient localization in complex environments. |
| title | Adaptive Motorized LiDAR Scanning Control for Robust Localization with OpenStreetMap |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.11742 |