LiDAR Point Cloud Registration with Formal Guarantees
LiDAR Point Cloud Registration with Formal Guarantees
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DOI：
10.1109/cdc51059.2022.9992625
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Matteo Marchi;Jonathan Bunton;B. Gharesifard;P. Tabuada
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文献类型：
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作者：
Matteo Marchi;Jonathan Bunton;B. Gharesifard;P. Tabuada
近年来，激光雷达传感器在自动驾驶系统的定位任务解决方案中变得越来越普遍。使用激光雷达数据进行定位的一个关键步骤是从不同姿势拍摄的两个激光雷达扫描的对齐，这个过程称为扫描匹配或点云配准。大多数针对该问题的现有算法本质上是启发式的和局部的，这意味着它们在初始化不良的情况下可能无法产生准确的结果。此外，现有的方法不能保证其输出的质量，这可能对安全关键任务有害。在本文中，我们分析了一种简单的点云配准算法，称为PASTA。该算法是全局的，不依赖于激光雷达数据中通常不存在的点对点对应。此外，据我们所知，我们提供了第一个具有可证明误差界限的点云配准算法。最后，我们在一个简单的轨迹跟踪任务的仿真中说明了所提出的算法和误差范围。
In recent years, LiDAR sensors have become pervasive in the solutions to localization tasks for autonomous systems. One key step in using LiDAR data for localization is the alignment of two LiDAR scans taken from different poses, a process called scan-matching or point cloud registration. Most existing algorithms for this problem are heuristic in nature and local, meaning they may not produce accurate results under poor initialization. Moreover, existing methods give no guarantee on the quality of their output, which can be detrimental for safety-critical tasks. In this paper, we analyze a simple algorithm for point cloud registration, termed PASTA. This algorithm is global and does not rely on point-to-point correspondences, which are typically absent in LiDAR data. Moreover, and to the best of our knowledge, we offer the first point cloud registration algorithm with provable error bounds. Finally, we illustrate the proposed algorithm and error bounds in simulation on a simple trajectory tracking task.