  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.
