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Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
arXiv - CS - Robotics Pub Date : 2020-04-03 , DOI: arxiv-2004.01374
Alexander Carballo, Abraham Monrroy, David Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, and Kazuya Takeda

In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware. LiDAR data used in this study is a subset of our LiDAR Benchmarking and Reference (LIBRE) dataset, captured independently from each sensor, from a vehicle driven on public urban roads multiple times, at different times of the day. In this study, we analyze the performance and characteristics of each LiDAR for the tasks of (1) 3D mapping including an assessment map quality based on mean map entropy, and (2) 6-DOF localization using a ground truth reference map.

中文翻译:

使用正态分布变换表征用于定位和映射的多个 3D LiDAR

在这项工作中,我们使用实施的正态分布变换 (NDT) 算法作为通用参考,对十种不同的 3D LiDAR 传感器进行了详细比较,涵盖了一系列制造商、型号和激光配置,用于映射和车辆定位任务在自动驾驶开源平台 Autoware 中。本研究中使用的 LiDAR 数据是我们的 LiDAR 基准和参考 (LIBRE) 数据集的一个子集,从每个传感器独立捕获,从在一天中的不同时间多次在公共城市道路上行驶的车辆中捕获。在这项研究中,我们分析了每个 LiDAR 在以下任务中的性能和特征:(1)3D 映射,包括基于平均地图熵的评估地图质量,以及(2)使用地面实况参考地图的 6-DOF 定位。
更新日期:2020-04-06
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