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Article type: Research Article
Authors: dos Santos, Gilberto Antonio Marcon | Ferrão, Victor Terra | Vinhal, Cássio* | da Cruz Júnior, Gélson
Affiliations: Laboratory for Education and Innovation on Automation, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia, Brazil
Correspondence: [*] Corresponding author: Cássio Vinhal, Laboratory for Education and Innovation on Automation, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia, Brazil. E-mail: vinhal@ufg.br
Abstract: This paper proposes a fast algorithm for ground segmentation of point clouds. It differentiates ground points from obstacle points from unstructured point clouds. This segmentation consists of performing an orthogonal projection into the horizontal xy plane followed by a top-down area subdivision. This process self-adapts to the point cloud, focusing processing effort on detailed areas. Real-time performance for execution in low cost embedded devices is demonstrated through quantitative and qualitative test results.
Keywords: Point cloud segmentation, ground extraction, sample consensus, evolution strategies, segmentation self-adaptation
DOI: 10.3233/HIS-160218
Journal: International Journal of Hybrid Intelligent Systems, vol. 12, no. 4, pp. 229-243, 2015
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