The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus (RANSAC) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐RANSAC) algorithm by removing points that do not belong to the roof plane. I‐RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC.

中文翻译：

---

从机载激光雷达数据中提取屋顶平面的改进RANSAC算法

从激光雷达数据提取建筑物屋顶平面已成为一个流行的研究主题，随机样本共识（RANSAC）是最常用的算法之一。RANSAC提取完整平面，当在平面边界之外但在平面空间内还有其他点时，这是有问题的。这项研究提出了一种改进的RANSAC（I‐RANSAC）算法，该算法通过删除不属于屋顶平面的点。I-RANSAC从提取的屋顶平面中选择一个随机点，然后在给定的阈值内搜索其邻居，以识别并消除异常值。在来自两个数据集的14座建筑物中测试了新算法，其中质量控制措施显示出比标准RANSAC有了显着改进。