Abstract
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 These priors enhance global consistency for robot navigation, but OSM is often incomplete or outdated, limiting its reliability in real-world deployment.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.
Abstract (translated)
LiDAR到OpenStreetMap（OSM）定位技术越来越受到关注，因为OSM提供了轻量级的全球先验信息，例如建筑物轮廓。这些先验知识增强了机器人导航中的全局一致性，但OSM通常不完整或过时，这限制了其在实际部署中的可靠性。同时，LiDAR本身受限于有限的视野（FoV），常用的方法是通过电机旋转来实现全景覆盖。然而，现有的电机驱动式LiDAR系统通常采用恒定速度扫描方式，这种方法忽略了场景结构和地图先验信息，导致在特征稀疏区域浪费了扫描资源，并降低了定位精度。 为了解决这些挑战，我们提出了一种基于OSM引导的自适应LiDAR扫描框架。该框架结合全局先验知识与局部可观测性预测来提高定位鲁棒性。具体而言，我们在不确定性感知模型预测控制中增加了一个带有OSM感知项的方法，可以根据场景依赖性的可观测性和OSM特征的空间分布灵活分配扫描工作量。 本方法在ROS平台上实现，并采用电机驱动式LiDAR里程计后端，在仿真和真实世界实验中进行了评估。在校园道路、室内走廊和城市环境中的测试结果表明，与恒定速度基准相比，轨迹误差显著降低，同时保持了完整的扫描覆盖率。这些发现突显了将开源地图与自适应LiDAR扫描结合使用以实现在复杂环境中稳健且高效定位的潜力。
URL
https://arxiv.org/abs/2509.11742
