Rigid Point Cloud Registration (PCR) algorithms aim to estimate the 6-DOF relative motion between two point clouds, which is important in various fields, including autonomous driving. Recent years have seen a significant improvement in global PCR algorithms, i.e. algorithms that can handle a large relative motion. This has been demonstrated in various scenarios, including indoor scenes, but has only been minimally tested in the Automotive setting, where point clouds are produced by vehicle-mounted LiDAR sensors. In this work, we aim to answer questions that are important for automotive applications, including: which of the new algorithms is the most accurate, and which is fastest? How transferable are deep-learning approaches, e.g. what happens when you train a network with data from Boston, and run it in a vehicle in Singapore? How small can the overlap between point clouds be before the algorithms start to deteriorate? To what extent are the algorithms rotation invariant? Our results are at times surprising. When comparing robust parameter estimation methods for registration, we find that the fastest and most accurate is not one of the newest approaches. Instead, it is a modern variant of the well known RANSAC technique. We also suggest a new outlier filtering method, Grid-Prioritized Filtering (GPF), to further improve it. An additional contribution of this work is an algorithm for selecting challenging sets of frame-pairs from automotive LiDAR datasets. This enables meaningful benchmarking in the Automotive LiDAR setting, and can also improve training for learning algorithms.
翻译:硬点云注册( PCR) 算法旨在估计两个点云之间的6-DOF相对运动,这在各个领域都很重要, 包括自主驾驶。 近些年来, 全球 PCR 算法, 也就是能够处理大相对运动的算法有了显著的改进。 这在各种假设中都得到了证明, 包括室内场景, 但是在汽车环境下, 点云是由车载LIDAR传感器生成的, 而在汽车环境中, 点云是由车载LIDAR 传感器生成的。 在这项工作中, 我们的目标是回答对于汽车应用来说很重要的问题, 包括: 哪些新算法最准确, 哪些最快? 深层次的学习方法是如何转移的, 例如, 当您用波士顿的数据来训练网络, 在新加坡的车辆中运行时会发生什么变化? 当算法开始恶化之前, 点云之间的重叠有多大? 我们的结果有时令人吃惊。 在比较稳健的参数估算方法时, 我们发现, 快速和最准确的不是最新的方法之一 。 相反,, 也就是用这个数字AAR 方法的现代变法,, 也显示它是一个已知的升级法。