The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022
 
30 May 2022
30 May 2022

ROBUST APPROACH FOR URBAN ROAD SURFACE EXTRACTION USING MOBILE LASER SCANNING 3D POINT CLOUDS

A. Nurunnabi1, F. N. Teferle1, R. C. Lindenbergh2, J. Li3, and S. Zlatanova4 A. Nurunnabi et al.
  • 1Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6, rue Richard Codenhove-Kalergi, L-1359 Luxembourg
  • 2Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
  • 3Geography and Environmental Management, University of Waterloo, Waterloo ON N2L 3G1, Canada
  • 4The School of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia

Keywords: Autonomous Driving, Curb, Filtering, Intelligent Transportation, Mobile Mapping, Road Safety, Robust Regression

Abstract. Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.