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Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments

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Book cover Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9095))

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Abstract

This paper addresses classification of 3D point cloud data from natural environments based on voxels. The proposed model uses multi-layer perceptrons to classify voxels based on a statistic geometric analysis of the spatial distribution of inner points. Geometric features such as tubular structures or flat surfaces are identified regardless of their orientation, which is useful for unstructured or natural environments. Furthermore, the combination of voxels and neural networks pursues faster computation than alternative strategies. The model has been successfully tested with 3D laser scans from natural environments.

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Author information

Authors and Affiliations

  1. Departamento de Ingeniería de Sistemas y Automática, Universidad de Málaga, Andalucía Tech, 29071, Málaga, Spain

    Victoria Plaza, Anthony Mandow & Alfonso Garcia-Cerezo

  2. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Andalucía Tech, 29071, Málaga, Spain

    Jose Antonio Gomez-Ruiz

Authors
  1. Victoria Plaza
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  2. Jose Antonio Gomez-Ruiz
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  3. Anthony Mandow
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  4. Alfonso Garcia-Cerezo
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Corresponding author

Correspondence to Jose Antonio Gomez-Ruiz .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Electronics Technology, University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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Plaza, V., Gomez-Ruiz, J.A., Mandow, A., Garcia-Cerezo, A. (2015). Multi-layer Perceptrons for Voxel-Based Classification of Point Clouds from Natural Environments. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-19222-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19221-5

  • Online ISBN: 978-3-319-19222-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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