无需标定目标，实现旋转激光雷达与电机的精准标定与鲁棒定位。
Accurate Calibration and Robust LiDAR-Inertial Odometry for Spinning Actuated LiDAR Systems
- 基于DH参数的无标定目标标定方法，适配多种安装姿态。
- 自适应调整下采样率与地图分辨率，在无特征区域保持定位稳定。
- 支持高速扫描，提升完整度，适合实际移动机器人应用。
精确标定与鲁棒定位是旋转式激光雷达下游任务的基础。现有方法需针对不同安装方式参数化外参，泛化性受限；且旋转激光雷达不可避免扫描无特征区域，难以平衡扫描覆盖与定位鲁棒性。为此，本文提出一种基于Denavit-Hartenberg（DH）准则的无标定目标激光雷达-电机标定（LM-Calibr），可适配多种安装配置。大量实验验证其在不同场景、安装角度和初始值下的精度与收敛性。此外，提出环境自适应激光雷达-惯性里程计（EVA-LIO），根据空间尺度自适应选择下采样率与地图分辨率，使执行器以最高速运行，提升扫描完整性，即使在短暂扫描无特征区域时仍能保证定位鲁棒性。源码与硬件设计已开源：github.com/zijiechenrobotics/lm_calibr，视频演示见youtu.be/cZyyrkmeoSk。
原文摘要 · Abstract (English)
Accurate calibration and robust localization are fundamental for downstream tasks in spinning actuated LiDAR applications. Existing methods, however, require parameterizing extrinsic parameters based on different mounting configurations, limiting their generalizability. Additionally, spinning actuated LiDAR inevitably scans featureless regions, which complicates the balance between scanning coverage and localization robustness. To address these challenges, this letter presents a targetless LiDAR-motor calibration (LM-Calibr) on the basis of the Denavit-Hartenberg convention and an environmental adaptive LiDAR-inertial odometry (EVA-LIO). LM-Calibr supports calibration of LiDAR-motor systems with various mounting configurations. Extensive experiments demonstrate its accuracy and convergence across different scenarios, mounting angles, and initial values. Additionally, EVA-LIO adaptively selects downsample rates and map resolutions according to spatial scale. This adaptivity enables the actuator to operate at maximum speed, thereby enhancing scanning completeness while ensuring robust localization, even when LiDAR briefly scans featureless areas. The source code and hardware design are available on GitHub: \textcolor{blue}{\href{https://github.com/zijiechenrobotics/lm_calibr}{github.com/zijiechenrobotics/lm\_calibr}}. The video is available at \textcolor{blue}{\href{https://youtu.be/cZyyrkmeoSk}{youtu.be/cZyyrkmeoSk}}
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