An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
复制标题
基于正态分布变换单元的改进RANSAC 3D点云平面分割
DOI：
10.3390/rs9050433
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发表时间：
2017-05-01
期刊：
影响因子：
5
通讯作者：
Tang, Lei
中科院分区：
文献类型：
--
作者：
Li, Lin;Yang, Fan;Tang, Lei
平面分割是从激光扫描仪获取的无组织点云自动重建室内和城市环境的一项基本任务。作为最常见的平面分割方法之一，标准随机抽样一致性（RANSAC）通常用于逐个连续检测平面。然而，由于随机抽取包含3个点的最小子集存在不确定性，当存在噪声和离群值时，它会受到伪平面问题的困扰。本研究提出了一种基于正态分布变换（NDT）单元的改进RANSAC方法，用于3D点云平面分割以避免伪平面。在每次迭代中选择一个平面NDT单元作为最小样本，以确保在同一平面上采样的正确性。3D NDT用一组NDT单元表示点云，并在每个单元内用正态分布对观测点进行建模。利用NDT单元的几何外观将NDT单元分为平面单元和非平面单元。所提方法在三个室内场景中得到了验证。实验结果表明，其正确率超过88.5%，完整率超过85.0%，这表明所提方法比标准RANSAC能识别出更可靠、更准确的平面，并且执行速度更快。这些结果验证了该方法的适用性。
Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.