  Point cloud registration is a prerequisite for many applications in computer
vision and robotics. Most existing methods focus on pairwise registration of
two point clouds with high overlap. Although there have been some methods for
low overlap cases, they struggle in degraded scenarios. This paper introduces a
novel framework dubbed L-PR, designed to register unordered low overlap
multiview point clouds leveraging LiDAR fiducial markers. We refer to them as
LiDAR fiducial markers, but they are the same as the popular AprilTag and ArUco
markers, thin sheets of paper that do not affect the 3D geometry of the
environment. We first propose an improved adaptive threshold marker detection
method to provide robust detection results when the viewpoints among point
clouds change dramatically. Then, we formulate the unordered multiview point
cloud registration problem as a maximum a-posteriori (MAP) problem and develop
a framework consisting of two levels of graphs to address it. The first-level
graph, constructed as a weighted graph, is designed to efficiently and
optimally infer initial values of scan poses from the unordered set. The
second-level graph is constructed as a factor graph. By globally optimizing the
variables on the graph, including scan poses, marker poses, and marker corner
positions, we tackle the MAP problem. We conduct both qualitative and
quantitative experiments to demonstrate that the proposed method surpasses
previous state-of-the-art (SOTA) methods and to showcase that L-PR can serve as
a low-cost and efficient tool for 3D asset collection and training data
collection. In particular, we collect a new dataset named Livox-3DMatch using
L-PR and incorporate it into the training of the SOTA learning-based method,
SGHR, which brings evident improvements for SGHR on various benchmarks.
