Robust statistical approaches for circle fitting in laser scanning three-dimensional point cloud data. (September 2018)

• Record Type: Journal Article
• Authors: Nurunnabi, Abdul; Sadahiro, Yukio; Laefer, Debra F.
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 417
• Page End: 431
• Publication Date: 2018-09
• Subjects: 3D modeling -- Feature extraction -- Object detection -- Point cloud processing -- Remote sensing -- Robust statistics -- Surface fitting; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.04.010
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Two robust circle fitting algorithms are proposed in point cloud data. The new methods fit robust circle in the presence noise, and high percentage of scattered and clustered outliers. The proposed methods fit and reconstruct circles for partial as well as full arc data. They are more accurate and robust than existing robust statistical methods like LTS and LMS, pattern recognition technique: LTSD, and computer vision techniques like RANSAC. The algorithms potential include building information modeling, product quality control, arboreal assessment and road asset monitoring. Abstract: 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.