Curb Detection and Accessibility Evaluation from Low-Density Mobile Mapping Point Cloud Data
Kiichiro Ishikawa*,†, Daisuke Kubo**, and Yoshiharu Amano**
*Nippon Institute of Technology
4-1 Gakuendai, Miyashiro-machi, Saitama 345-8501, Japan
**Waseda University, Tokyo, Japan
Our goal is to automatically classify objects from Mobile Mapping System data to enable the automatic construction of dynamic maps. We aimed at the extraction of curbstones and classification of curb types. Although there is much research about curbstones being recognized from laser-scanned point clouds, there are few methods to classify curb types. In this paper, we propose a method to extract curbstones from low-density-type laser scan data. We also propose a method to distinguish whether curbstones allow access to off-road facilities. Evaluation tests give an F-measure of ≥94.4% and an accessibility classification accuracy of ≥99.6%. Moreover, the results of applying multiple filters to noise removal are compared.
-  S. Kuzumaki, “SIP Automated Driving System – SIP Automated Driving for Universal Service (SIP-adus) –,” Special Feature ICT for the Next Generation ITS, New Breeze 2015, No.3, pp. 9-11, 2015.
-  K. Ishikawa, T. Onishi, Y. Amano, T. Hashizume, J. Takiguchi, T. Fujishima, and Y. Tanaka, “Development of a vehicle-mounted road surface 3D measurement system,” Proc. of the 23rd Int. Symposium on Robotics and Automation in Construction, ISARC, pp. 569-573, 2006.
-  T. Watanabe, T. Niwa, and H. Masuda, “Registration of Point-Clouds from Terrestrial and Portable Laser Scanners,” Int. J. of Automation Technology, Vol.10, No.2, pp. 163-171, 2016.
-  K. Ishikawa, F. Tonomura, Y. Amano, and T. Hashizume, “Recognition of Road Objects from 3D Mobile Mapping Data,” Int. J. of CAD/CAM, Vol.13, No.2, 2013.
-  K. Kohira, K. Fukano, and H. Masuda, “Segmentation and Recognition of Pole-like Objects from Mobile Laser Scanning Data,” Proc. of The 16th Int. Conf. on Precision Engineering (ICPE2016), 2016.
-  H. Yokoyama, H. Date, S. Kanai, and H. Takeda, “Detection and Classification of Pole-like Objects from Mobile Laser Scanning Data of Urban Environments,” Int. J. of CAD/CAM, Vol.13, No.1, pp. 1-10, 2013.
-  C. Ordóñez, C. Cabo, and E. Sanz-Ablanedo, “Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data,” Sensors 2017, doi: 10.3390/s17071465, 2017.
-  T. Michalke, R. Kastner, I. Fritsch, and C. Goerick, “A Self-Adaptive Approach for Curbstone Roadside Detection based on Humanlike Signal Processing and Multi-Sensor Fusion,” IEEE Intelligent Vehicles Symp. (IV), 2010.
-  R. Huang, J. Chen, J. Liu, L. Liu, B. Yu, and Y. Wu, “Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle Intelligent Transportation Systems,” Information, Vol.8, Issue 3, p. 93, doi: 10.3390/info8030093, 2017.
-  A. Y. Hata, F. S. Osorio, and D. F. Wolf, “Robust Curb Detection and Vehicle Localization in Urban Environments,” Proc. of 2014 IEEE Intelligent Vehicles Symp., 2014.
-  W. Rong-ben, G. Bai-yuan, J. Li-sheng, Y. Tian-hong, and G. Lie, “Study on curb detection method based on 3D range image by Laser Radar,” IEEE Intelligent Vehicles Symp., 2005.
-  R. Miyazaki, M. Yamamoto, and K. Harada, “Polygonal Model Creation with Precise Boundary Edges from a Mobile Mapping Data,” IJCSNS Int. J. of Computer Science and Network Security, Vol.16, No.11, 2016.
-  J. He and H. Masuda, “Reconstruction of Roadways and Walkways Using Point-Clouds from Mobile Mapping System,” 2012 Asian Conf. on Design and Digital Engineering (ACDDE 2012), 2012.
-  H. Wang, H. Luo, C. Wen, J. Cheng, P. Li, Y. Chen, C. Wang, and J. Li, “Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data,” IEEE Geoscience and Remote Sensing Letters, Vol.12, Issue 10, pp. 2085-2089, 2015.
-  A. Serna and B. Marcotegui, “Urban accessibility diagnosis from mobile laser scanning data,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.84, pp. 23-32, 2013.
-  N. Akkiraju, H. Edelsbruner, M. Facello, P. Fu, E. P. Mücke, and C. Varela, “Alpha Shapes: Definition and Software,” Proc. Internet Comput. Geom. Software Workshop, 1995.
-  H. Edelsbrunner, “Smooth surfaces for multi-scale shape representation,” Foundations of software technology and theoretical computer science (Bangalore, 1995), Lecture Notes in Comput. Sci., Vol.1026, Springer, pp. 391-412, 1995.
-  J. Lodder, “Curvature in the Calculus Curriculum,” The American Mathematical Monthly, Vol.110, No.7, pp. 593-605, 2003.
-  P. Nooralishahi and C. K. Loo, “3D Object Detection for Reconstructed Scene Using Multi-layer Growing Neural Gas, Computational Intelligence in Information Systems,” Advances in Intelligent Systems and Computing, pp. 211-221, 2014.
-  K. Pearson, “On Lines and Planes of Closest Fit to Systems of Points in Space,” Philosophical Magazine, Vol.2, No.11, pp. 559-572, 1901.
-  H. Hotelling, “Relations between two sets of variates,” Biometrika, Vol.28, pp. 321-377, 1936.
-  H. Abdi, L. J. Williams, “Principal component analysis,” Wiley Interdisciplinary Reviews: Computational Statistics, Vol.2, pp. 433-459, 2010.
-  H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. of Educational Psychology, Vol.24, pp. 417-441, 1933.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.