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Region-Growing Planar Segmentation for Robot Action Planning

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AI 2015: Advances in Artificial Intelligence (AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9457))

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Abstract

Object detection, classification and manipulation are some of the capabilities required by autonomous robots. The main steps in object classification are: segmentation, feature extraction, object representation and learning. To address the problem of learning object classification using multi-view range data, we used a relational approach. The first step of our object classification method is to decompose a scene into shape primitives such as planes, followed by extracting a set of higher-level, relational features from the segmented regions. In this paper, we compare our plane segmentation algorithm with state-of-the-art plane segmentation algorithms which are publicly available. We show that our segmentation outperforms visually and also produces better results for the robot action planning.

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Notes

  1. 1.

    http://pointclouds.org/documentation/tutorials/planar_segmentation.php.

  2. 2.

    http://docs.pointclouds.org/1.7.0/group__sample__consensus.html .

  3. 3.

    http://pointclouds.org/documentation/tutorials/cylinder_segmentation.php.

  4. 4.

    http://rfarid.altervista.org/plane_seg_compare/comp.html.

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Acknowledgement

We thank the people who kindly participated on visual comparison between our method and SNP.

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Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, 170 Kessels Rd, Nathan, QLD, 4111, Australia

    Reza Farid

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  1. Reza Farid
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Correspondence to Reza Farid .

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Editors and Affiliations

  1. The University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Jochen Renz

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Farid, R. (2015). Region-Growing Planar Segmentation for Robot Action Planning. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-26350-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26349-6

  • Online ISBN: 978-3-319-26350-2

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