标题： 激光雷达变异性：一种新型数据集和固态与旋转激光雷达的比较研究 显示英文标题

标题： Lidar Variability: A Novel Dataset and Comparative Study of Solid-State and Spinning Lidars

摘要： 激光雷达技术已被广泛应用于各种领域，例如在无GNSS环境中的机器人定位和三维重建。 最近的进展引入了不同类型的激光雷达，包括成本效益高的固态激光雷达，如Livox Avia和Mid-360。 Mid-360因其圆顶状设计，由于其低成本、紧凑尺寸和可靠性能，越来越多地用于便携式地图绘制和无人驾驶飞行器（UAV）应用。 然而，缺乏包含圆顶状激光雷达（如Mid-360）以及其他固态和旋转激光雷达的数据集，严重阻碍了在不同平台上的新方法比较评估。 此外，低成本固态激光雷达和高端旋转激光雷达（例如Ouster OS系列）之间的性能差异尚未得到充分研究，尤其是在里程计中没有惯性测量单元（IMU）的情况下。 为解决这一问题，我们引入了一个包含多种激光雷达类型的新数据集，包括低成本的Livox Avia和圆顶状的Mid-360，以及高端旋转激光雷达，如Ouster系列。 值得注意的是，据我们所知，目前没有现有数据集全面包含如Mid-360这样的圆顶状激光雷达，以及其他的固态和旋转激光雷达。 除了数据集外，我们还提供了针对此多样化传感器数据的最先进SLAM算法的基准评估。 此外，我们还使用从包含的激光雷达系统中收集的室内和室外数据，对点云配准技术进行了定量分析，具体包括点对点、点对面和混合方法。 本研究的结果为跨异构激光雷达平台的SLAM和三维重建的未来研究提供了一个基础参考。 显示英文摘要

摘要： Lidar technology has been widely employed across various applications, such as robot localization in GNSS-denied environments and 3D reconstruction. Recent advancements have introduced different lidar types, including cost-effective solid-state lidars such as the Livox Avia and Mid-360. The Mid-360, with its dome-like design, is increasingly used in portable mapping and unmanned aerial vehicle (UAV) applications due to its low cost, compact size, and reliable performance. However, the lack of datasets that include dome-shaped lidars, such as the Mid-360, alongside other solid-state and spinning lidars significantly hinders the comparative evaluation of novel approaches across platforms. Additionally, performance differences between low-cost solid-state and high-end spinning lidars (e.g., Ouster OS series) remain insufficiently examined, particularly without an Inertial Measurement Unit (IMU) in odometry. To address this gap, we introduce a novel dataset comprising data from multiple lidar types, including the low-cost Livox Avia and the dome-shaped Mid-360, as well as high-end spinning lidars such as the Ouster series. Notably, to the best of our knowledge, no existing dataset comprehensively includes dome-shaped lidars such as Mid-360 alongside both other solid-state and spinning lidars. In addition to the dataset, we provide a benchmark evaluation of state-of-the-art SLAM algorithms applied to this diverse sensor data. Furthermore, we present a quantitative analysis of point cloud registration techniques, specifically point-to-point, point-to-plane, and hybrid methods, using indoor and outdoor data collected from the included lidar systems. The outcomes of this study establish a foundational reference for future research in SLAM and 3D reconstruction across heterogeneous lidar platforms.