LiDAR-based mapping/reconstruction are important for various applications,
but evaluating the quality of the dense maps they produce is challenging. The
current methods have limitations, including the inability to capture
completeness, structural information, and local variations in error. In this
paper, we propose a novel point quality evaluation metric (PQM) that consists
of four sub-metrics to provide a more comprehensive evaluation of point cloud
quality. The completeness sub-metric evaluates the proportion of missing data,
the artifact score sub-metric recognizes and characterizes artifacts, the
accuracy sub-metric measures registration accuracy, and the resolution
sub-metric quantifies point cloud density. Through an ablation study using a
prototype dataset, we demonstrate the effectiveness of each of the sub-metrics
and compare them to popular point cloud distance measures. Using three LiDAR
SLAM systems to generate maps, we evaluate their output map quality and
demonstrate the metrics robustness to noise and artifacts. Our implementation
of PQM, datasets and detailed documentation on how to integrate with your
custom dense mapping pipeline can be found at github.com/droneslab/pqm

本文提出了一个新的点云质量评估度量 (PQM)，包括完整度子度量、伪迹得分子度量、准确性子度量和分辨率子度量，通过一个原型数据集的消融研究显示出了每个子度量的有效性，并将其与流行的点云距离度量进行了比较。我们通过使用三个 LiDAR SLAM 系统生成地图，评估了它们的输出地图质量并证明了该指标对噪声和伪迹的稳健性。