Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data
Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data
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DOI：
10.1016/j.patcog.2018.04.010
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发表时间：
2018-09-01
影响因子：
8
通讯作者：
Laefer, Debra F.
中科院分区：
文献类型：
--
作者：
Nurunnabi, Abdul;Sadahiro, Yukio;Laefer, Debra F.
本文探讨了存在异常点的不完全（部分弧）激光扫描点云数据的圆拟合问题。在移动激光扫描中，由于扫描单元对测量对象的方位和有限的街道位置，数据通常是不完整的。此外，建筑环境中的多个结构经常产生聚集的异常值。为了解决这些问题，本文将鲁棒主成分分析（PCA）和鲁棒回归与一种高效的代数圆拟合方法相结合，提出了两种圆拟合算法。实验结果表明，所提出的算法具有统计鲁棒性，可以容忍高百分比（超过44%）的聚类异常值，误差水平不显著，同时与现有的竞争方法相比，仍然可以获得更好的形状识别。例如，对于包含20%聚类异常值的1000个四分之一圆数据集的模拟，RANSAC估计圆半径的均方误差（MSE）为172.10，而所提出的算法拟合圆的MSE小于0.42。这些算法在许多领域都有潜力，包括建筑信息建模、粒子跟踪、产品质量控制、树木评估和道路资产监控。(C) 2018 Elsevier Ltd.版权所有。
This paper explores the problem of circle fitting for incomplete (partial arc) laser scanning point cloud data in the presence of outliers. In mobile laser scanning, data are commonly incomplete because of the orientation of the scanning unit to the surveying objects and the limited street-based positions. Also, multiple structures in the built environment often produce clustered outliers. To address these problems, this paper combines robust Principal Component Analysis (PCA) and robust regression with an efficient algebraic circle fitting method to develop two algorithms for circle fitting. Experimental efforts show that the proposed algorithms are statistically robust and can tolerate a high-percentage (exceeding 44%) of clustered outliers with insignificant error levels, while still achieving better shape recognition compared to existing competitive methods. For example, for a simulation of 1000 quarter circle datasets including 20% clustered outliers, RANSAC estimated the circle radius with a Mean Squared Error (MSE) of 172.10, whereas the proposed algorithms fit circles with an MSE of less than 0.42. The algorithms have potential in many areas including building information modeling, particle tracking, product quality control, arboreal assessment, and road asset monitoring. (C) 2018 Elsevier Ltd. All rights reserved.