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}}

翻译：精确的标定与鲁棒的定位是旋转驱动激光雷达应用下游任务的基础。然而，现有方法需要根据不同安装构型对外参进行参数化，这限制了其泛化能力。此外，旋转驱动激光雷达不可避免地会扫描到缺乏特征信息的区域，这使得扫描覆盖范围与定位鲁棒性之间的平衡变得复杂。为应对这些挑战，本文提出了一种基于Denavit-Hartenberg约定的无目标激光雷达-电机标定方法（LM-Calibr）以及一种环境自适应的激光雷达-惯性里程计（EVA-LIO）。LM-Calibr支持对多种安装构型的激光雷达-电机系统进行标定。大量实验证明了其在不同场景、安装角度和初始值下的精度与收敛性。此外，EVA-LIO能根据空间尺度自适应选择下采样率与地图分辨率。这种自适应性使得驱动器能够以最大速度运行，从而在即使激光雷达短暂扫描到无特征区域时，也能在确保鲁棒定位的同时提升扫描完整性。源代码与硬件设计已发布于GitHub：\textcolor{blue}{\href{https://github.com/zijiechenrobotics/lm_calibr}{github.com/zijiechenrobotics/lm\_calibr}}。演示视频可在\textcolor{blue}{\href{https://youtu.be/cZyyrkmeoSk}{youtu.be/cZyyrkmeoSk}}查看。