Adaptive Motorized LiDAR Scanning Control for Robust Localization with OpenStreetMap

2025-09-15

AI Summary

Poor robustness of LiDAR localization stems from incomplete/outdated OpenStreetMap (OSM) data and fixed scan rates that fail to adapt to scene structure and prior knowledge. To address this, we propose an adaptive LiDAR scanning control framework that jointly leverages global OSM priors and local observability prediction. Our method innovatively integrates OSM-derived structural priors into an uncertainty-aware Model Predictive Control (MPC) formulation, enabling dynamic allocation of scanning resources—particularly enhancing localization reliability in feature-sparse regions. We implement a fully integrated system in ROS, combining motorized LiDAR odometry, OSM-based map matching, and closed-loop adaptive scanning. Extensive evaluations across campus roads, indoor corridors, and urban environments—both in simulation and real-world deployment—demonstrate significant reductions in trajectory error while preserving full-scene coverage, thereby validating the effectiveness and robustness of the proposed approach.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 These priors enhance global consistency for robot navigation, but OSM is often incomplete or outdated, limiting its reliability in real-world deployment.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.

Problem

• Adaptive LiDAR scanning for robust localization with incomplete OSM
• Overcoming constant-speed scanning limitations in feature-sparse regions
• Integrating OSM priors with local observability for improved accuracy

Innovation

• Adaptive motorized LiDAR scanning with OSM guidance
• Uncertainty-aware model predictive control with OSM-aware term
• Dynamic scanning effort allocation based on observability and features