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 named 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 qualitative and quantitative
experiments to demonstrate that the proposed method exhibits superiority over
competitors in four aspects: registration accuracy, instance reconstruction
quality, localization accuracy, and robustness to the degraded scene. To
benefit the community, we open-source our method and dataset at
this https URL

本研究介绍了一种名为 L-PR 的新型框架，利用 LiDAR 基准标志（类似于 AprilTag 和 ArUco 标记）来注册无序的低重叠多视点点云。通过改进的自适应阈值标记检测方法和二级图优化，该方法在准确性、重建质量、定位准确性和对恶化场景的鲁棒性等四个方面表现优越。我们将方法和数据集开源，链接：https://this_URL。