Abstract
This paper proposes a 3D point cloud segmentation method using a reflection intensity of Laser Range Finder (LRF). In this paper, we use LRF and tilt unit for acquiring a 3D point cloud. First of all, we apply Growing Neural Gas (GNG) to the point cloud for learning a topological structure of the point cloud. Next, we proposed a segmentation method based on an intensity histogram that is composed of the nearest data of each node. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.
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References
Sithole, G., Mapurisa, W.T.: 3D object segmentation of point clouds using profiling techniques. S. Afr. J. Geomatics 1(1), 60–76 (2012)
Khan, A.A., Riaz, S., Iqbal, J.: Surface estimation of a pedestrian walk for outdoor use of power wheelchair based robot. Life Sci. J. 10, 1697–1704 (2013)
To, A.W.K., Paul, G., Liu, D.: Surface-type classification using RGB-D. IEEE Trans. Autom. Sci. Eng. 11, 359–366 (2014)
Kim, B., Xu, S., Savarese, S.: Accurate localization of 3D objects from RGB-D data using segmentation hypotheses. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3182–3189 (2013)
Diebold, J., Demmel, N., Hazırbaş, C., Moeller, M., Cremers, D.: Interactive multi-label segmentation of RGB-D images. In: Aujol, J.-F., Nikolova, M., Papadakis, N. (eds.) SSVM 2015. LNCS, vol. 9087, pp. 294–306. Springer, Heidelberg (2015)
Richtsfeld, A., Mrwald, T., Prankl, J., Zillich, M., Vincze, M.: Learning of perceptual grouping for object segmentation on RGB-D data. J. Vis. Commun. Image Repersentation 25(1), 64–73 (2014)
Hara, Y., Kawata, H., Ohya, A., Yuta, S.: Mobile robot localization and mapping by scan matching using laser reflection intensity of the SOKUIKI sensor. In: 32nd Annual Conference on IEEE Industrial Electronics, IECON 2006, pp. 3018–3023 (2006)
Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems, vol. 7, pp. 625–632 (1995)
Choi, B., Mericli, C., Biswasa, J., Veloso, M.M.: Fast human detection for indoor mobile robots using depth images. In: Advances in Neural Information Processing Systems, vol. 7, pp. 625–632 (1995)
Oishi, S., Kurazume, R., Iwashita, Y., Hasegawa, T.: Colorization of 3D geometric model utilizing laser reflectivity. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2319–2326 (2013)
HOKUYO AUTOMATICCO, LTD. http://www.hokuyo-aut.co.jp/
Fritzke, B.: Growing cell structures - a self-organising network for unsupervised and supervised learning. Neural Netw. 7(9), 1441–1460 (1994)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2000)
Martinetz, T.M., Schulten, K.J.: “A neural-gas” network learns topologies. In: Artificial Neural Networks, vol. 1, pp. 397–402 (1991)
Fritzke, B.: Growing self-organizing networks why? In: European Symposium on Artificial, Neural Networks, pp. 61–72 (1996)
Acknowledgment
This work was funded by ImPACT Program of the Council for Science, Technology and Innovation.
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Miyake, S., Toda, Y., Kubota, N., Takesue, N., Wada, K. (2016). Intensity Histogram Based Segmentation of 3D Point Cloud Using Growing Neural Gas. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9835. Springer, Cham. https://doi.org/10.1007/978-3-319-43518-3_33
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DOI: https://doi.org/10.1007/978-3-319-43518-3_33
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