Volume XXXVIII-5/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 115-120, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-115-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 115-120, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-115-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Sep 2012

03 Sep 2012

POLE-LIKE OBJECTS RECOGNITION FROM MOBILE LASER SCANNING DATA USING SMOOTHING AND PRINCIPAL COMPONENT ANALYSIS

H. Yokoyama1, H. Date1, S. Kanai1, and H. Takeda2 H. Yokoyama et al.
  • 1Graduate School of Information Science and Technology, Hokkaido University, Sapporo060-0814, Japan
  • 2Kokusai Kogyo Co., Ltd, Chiyoda-ku102-0085, Japan

Keywords: Mobile Laser Scanning, Object Recognition, Laplacian Smoothing, Point Clouds, Principal Component Analysis, Pole-like Objects

Abstract. With the spread of the Mobile Laser Scanning (MLS) system, the demands for the management of road and facilities using MLS point clouds have increased. Especially, pole-like objects such as streetlights, utility poles, street signs and etc. are in high demand as facilities to be managed. We propose a method for recognizing pole-like objects from MLS point clouds. Our method is based on Laplacian smoothing using the k-nearest neighbors graph, Principal Component Analysis for recognizing points on pole-like objects, and thresholding for the degree of pole-like objects. Our method can robustly recognize pole-like objects with various radii and tilt angles from MLS point clouds. For correctly segmented objects, accuracy of pole-like object recognition is on average 97.4%.