3D LiDAR分辨率在图SLAM方法中的影响：一项比较研究
Impact of 3D LiDAR Resolution in Graph-based SLAM Approaches: A Comparative Study
AI总结：
本文比较了城市场景中SC-LeGO-LOAM、SC-LIO-SAM、Cartographer和HDL-Graph等3D LiDAR图SLAM方法，并利用KITTI和AUTONOMOS-LABS数据集评估64与128通道LiDAR分辨率对鲁棒性的影响。
摘要：
同步定位与建图（SLAM）是自主系统的关键组成部分，这些系统运行在需要一致地图以实现可靠定位的环境中。SLAM已被广泛研究数十年，大多数解决方案基于相机或LiDAR。早期的LiDAR方法主要依赖2D数据，而较新的框架使用3D数据。在本工作中，我们综述了城市场景中近期基于3D LiDAR的Graph-SLAM方法，旨在比较它们的优势、弱点和局限性。此外，我们评估了它们在LiDAR分辨率方面的鲁棒性，即64通道与128通道的对比。关于SLAM方法，我们使用KITTI里程计数据集（仅包含64通道LiDAR）和一个新数据集（AUTONOMOS-LABS）在真实城市场景中评估了SC-LeGO-LOAM、SC-LIO-SAM、Cartographer和HDL-Graph。后一个数据集使用在柏林郊区行驶的仪表化车辆采集，同时包含64通道和128通道LiDAR。实验结果以定量指标形式报告，并辅以定性地图。
英文摘要：
Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has been a widely studied topic for decades with most of the solutions being camera or LiDAR based. Early LiDAR-based approaches primarily relied on 2D data, whereas more recent frameworks use 3D data. In this work, we survey recent 3D LiDAR-based Graph-SLAM methods in urban environments, aiming to compare their strengths, weaknesses, and limitations. Additionally, we evaluate their robustness regarding the LiDAR resolution namely 64 $vs$ 128 channels. Regarding SLAM methods, we evaluate SC-LeGO-LOAM, SC-LIO-SAM, Cartographer, and HDL-Graph on real-world urban environments using the KITTI odometry dataset (a LiDAR with 64-channels only) and a new dataset (AUTONOMOS-LABS). The latter dataset, collected using instrumented vehicles driving in Berlin suburban area, comprises both 64 and 128 LiDARs. The experimental results are reported in terms of quantitative `metrics' and complemented by qualitative maps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
