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

翻译：摘要：基于激光雷达的建图/重建技术在各种应用中至关重要，但评估其生成的密集地图质量仍具挑战性。现有方法存在局限性，包括无法捕捉完整性、结构信息以及局部误差变化。本文提出一种新颖的点质量评估指标（PQM），该指标由四个子指标组成，可对点云质量进行更全面的评估。完整性子指标评估缺失数据比例，伪影分数子指标识别并表征伪影，精度子指标衡量配准准确性，分辨率子指标量化点云密度。通过使用原型数据集进行消融研究，我们证明了每个子指标的有效性，并将其与流行的点云距离度量进行了比较。利用三个激光雷达SLAM系统生成地图，我们评估了其输出地图质量，并证明了该指标对噪声和伪影的鲁棒性。PQM的实现、数据集以及如何集成到自定义密集建图流程的详细文档可在github.com/droneslab/pqm获取。