Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Modeling (BIM), and robot navigation. Motorized LiDAR systems can expand the Field of View (FoV) without adding multiple scanners, but existing motorized LiDAR systems often rely on constant-speed motor control, leading to suboptimal performance in complex environments. To address this, we propose UA-MPC, an uncertainty-aware motor control strategy that balances scanning accuracy and efficiency. By predicting discrete observabilities of LiDAR Odometry (LO) through ray tracing and modeling their distribution with a surrogate function, UA-MPC efficiently optimizes motor speed control according to different scenes. Additionally, we develop a ROS-based realistic simulation environment for motorized LiDAR systems, enabling the evaluation of control strategies across diverse scenarios. Extensive experiments, conducted on both simulated and real-world scenarios, demonstrate that our method significantly improves odometry accuracy while preserving the scanning efficiency of motorized LiDAR systems. Specifically, it achieves over a 60\% reduction in positioning error with less than a 2\% decrease in efficiency compared to constant-speed control, offering a smarter and more effective solution for active 3D sensing tasks. The simulation environment for control motorized LiDAR is open-sourced at: \url{https://github.com/kafeiyin00/UA-MPC.git}.

翻译：利用LiDAR系统实现精确且全面的三维感知，在设施检测、建筑信息模型（BIM）以及机器人导航等摄影测量与机器人学的多种应用中至关重要。电机驱动LiDAR系统无需增加多个扫描仪即可扩展视场（FoV），但现有系统通常依赖恒定转速的电机控制，导致在复杂环境中性能欠佳。为此，我们提出UA-MPC，一种不确定性感知的电机控制策略，旨在平衡扫描精度与效率。该方法通过光线追踪预测LiDAR里程计（LO）的离散可观测性，并利用代理函数对其分布进行建模，从而根据不同场景高效优化电机转速控制。此外，我们开发了一个基于ROS的电机驱动LiDAR系统高仿真模拟环境，支持在不同场景下评估控制策略。在仿真与真实场景中进行的大量实验表明，我们的方法在保持电机驱动LiDAR系统扫描效率的同时，显著提升了里程计精度。具体而言，与恒速控制相比，该方法在效率降低不足2%的情况下，实现了超过60%的定位误差降低，为主动三维感知任务提供了更智能、更有效的解决方案。用于控制电机驱动LiDAR的模拟环境已在以下地址开源：\url{https://github.com/kafeiyin00/UA-MPC.git}。