Robust cylinder fitting in three-dimensional point cloud data

Abdul Nurunnabi, Yukio Sadahiro, Roderik Lindenbergh

Research output: Contribution to journalConference articleScientificpeer-review

17 Citations (Scopus)
291 Downloads (Pure)

Abstract

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

Original languageEnglish
Pages (from-to)63-70
Number of pages8
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume42
Issue number1W1
DOIs
Publication statusPublished - 30 May 2017
EventISPRS Hannover Workshop 2017 - Leibniz Universität Hannover, Hannover, Germany
Duration: 6 Jun 20179 Jun 2017
https://www.ipi.uni-hannover.de/hrigi17.html

Keywords

  • Feature extraction
  • Geometric shape
  • Laser scanning
  • Object recognition
  • Pole modelling
  • Robust PCA
  • Robust regression
  • Surface fitting

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