Abstract
Several recent works deal with 3D data in mobile robotic problems: mapping and SLAM related problems. Data come from any kind of sensor (time of flight cameras and 3D lasers) providing a huge amount of unorganized 3D data. In this paper we detail an efficient method to build complete 3D models from a Growing Neural Gass (GNG). The GNG obtained is then applied to a sequence. From neurons in the GNG, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm.
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Viejo, D., Garcia, J., Cazorla, M. (2011). 6DoF Egomotion Computing Using 3D GNG-Based Reconstruction. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_7
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DOI: https://doi.org/10.1007/978-3-642-21498-1_7
Publisher Name: Springer, Berlin, Heidelberg
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