Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 63-70, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 63-70, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

A. Nurunnabi1, Y. Sadahiro1, and R. Lindenbergh2 A. Nurunnabi et al.
  • 1Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan
  • 2Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, the Netherlands

Keywords: Feature Extraction, Geometric Shape, Laser Scanning, Object Recognition, Pole Modelling, Robust PCA, Robust Regression, Surface Fitting

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.