Registration is one of the most important tasks in mobile laser scanning (MLS) point cloud processing. This paper firstly reviews existing target‐free matching techniques as well as methods to evaluate the quality of the registration. Next, a new error metric is introduced that takes into account the residuals of check planes as well as their orientation. Experiments using real datasets in combination with reference data were performed to evaluate the suitability of these metrics. The proposed error metric proved to be more suitable for evaluating the quality of point cloud registration than state‐of‐the‐art equivalents. The results also indicate that least squares plane fitting is the best technique for MLS point cloud registration.

中文翻译：

---

回顾使用改进的误差指标的城市移动激光扫描无目标配准方法

注册是移动激光扫描（MLS）点云处理中最重要的任务之一。本文首先回顾了现有的无目标匹配技术以及评估注册质量的方法。接下来，引入了一种新的误差度量，该度量考虑了检查平面的残差及其方向。进行了将真实数据集与参考数据结合使用的实验，以评估这些指标的适用性。事实证明，所提出的误差度量比最新的等效度量更适合评估点云注册的质量。结果还表明，最小二乘平面拟合是MLS点云配准的最佳技术。