Mobile terrestrial lidar data to detect traffic sign and light pole/ Dados de laser terrestre móvel para detectar placas de sinalização e postes de iluminação

Elizabete Bugalski de Andrade Peixoto, Jorge Antonio Silva Centeno


This paper introduces a method for detecting and classifying vertical objects from a mobile terrestrial laser scanner point cloud. The paper concentrates on the classification of the top of the poles, where shields or lamps are installed. First, the variance-covariance matrix of each segmented object is computed. Then the eigenvalues and eigenvector of this matrix are derived. The 3D coordinates of each point are then transformed using the principal components transform in order to compute new features in this new space. In the second step the distribution of the three eigenvalues of the different classes in the eigenvalues space is analysed. Is it deduced that similar objects align in this space, allowing proposing a classification rule based on the distance to the lines. An experiment was performed to verify the approach performance. In the classification of different objects, the global accuracy reached 75%. When the classification was more general, separating just flat from three-dimensional objects the accuracy reached 94%. From the obtained results it can be concluded that the proposed method is feasible and allows separating objects according to its shape


Mobile mapping, point cloud processing, pole mapping

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Denis, E.; Burck R.; Baillard, C. (2010). Towards road modelling from terrestrial laser points. In: Paparoditis N., Pierrot-Deseilligny M., Mallet C., Tournaire O. (Eds), IAPRS, Vol. XXXVIII, Part 3A – Saint-Mandé, France, September 1-3, 2010.

El-Halawany, S. (2013). Detection of Road Furniture from Mobile Terrestrial Laser ScanningPoint Clouds, Ph.D. thesis, Department of Geomatics Engineering,University of Calgary, Canada.

Fischler, M.A. and Boller, R. C. B., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communication of the ACM 24 (6), pp.381–395.

Fukano, K. and Masuda, H., 2015. Detection and Classification of Pole-Like Objects from Mobile Mapping Data. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, pp. 57-64.

Huang, J. and You, S., 2005. Pole-like object detection and classification from urban point clouds. IEEE International Conference on Robotics and Automation (ICRA), pp. 1-7.

Ibrahim, S. and Lichti, D. (2012). Curb-based street floor extraction from mobile terrestrial LiDAR point cloud. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B5, 2012. XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia.

Jutzi, B. and Gross, H., 2009. Nearest neighbour classification on laser point clouds to gain object structures from buildings. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII, Part 1-4-7/W5.

Li, F.; Elberink, O.; Vosselman, G. (2016). Pole-like street furniture decomposition in mobile laser scanning data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume III-3, 2016. XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.

Luo D. and Wang Y., 2008, Rapid extracting pillars by slicing point clouds, International Archives of the Photogrammetry Remote Sensing, vol.36-8, pp.71-75.

Ordóñez,C., Cabo, C. and Sanz-Ablanedo, E., 2017. Automatic detection and classification of pole-like objects for urban cartography using mobile LASER scanning data. Sensors, 17, 1465; pp 1-10.

Press P. and Austin, D. (2004). Approaches to pole detection using ranged laser data. Proceedings of Australasian Conference on Robotics and Automation, Citeseer. pp.1–8.

Reed, I.S.; Yu, X. (1990). Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 1990, 38, 1760–1770.

Rodríguez-Cuenca, B.; García-Cortés, S.; Ordóñez, C.; Alonso, M. C. (2015). Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm. Remote Sens. 2015, 7, 12680-12703.

Weinmann, M, Jutzi, B. and Mallet, C., 2013. Feature relevance assessment for the semantic interpretation of 3d point cloud data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013 ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey.

Yokoyama, H., Date, H., Kanai, S. and Takeda, H., 2013. Detection and classification of pole-like objects from mobile laser scanning data of urban environments. International Journal of CAD/CAM Vol. 13, No. 2, pp. 31–40.

Yu, Y., Li, J., Guan, H., Wang, C. and Yu, J., 2015. Semiautomated extraction of street light poles from mobile LiDAR point-clouds. IEEE Transactions On Geoscience And Remote Sensing, Vol. 53, No. 3, pp. 1374–1386.



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