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Geometric and visual terrain classification for autonomous mobile navigation
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2017, article id 8206092Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a multi-sensory terrain classification algorithm with a generalized terrain representation using semantic and geometric features. We compute geometric features from lidar point clouds and extract pixel-wise semantic labels from a fully convolutional network that is trained using a dataset with a strong focus on urban navigation. We use data augmentation to overcome the biases of the original dataset and apply transfer learning to adapt the model to new semantic labels in off-road environments. Finally, we fuse the visual and geometric features using a random forest to classify the terrain traversability into three classes: safe, risky and obstacle. We implement the algorithm on our four-wheeled robot and test it in novel environments including both urban and off-road scenes which are distinct from the training environments and under summer and winter conditions. We provide experimental result to show that our algorithm can perform accurate and fast prediction of terrain traversability in a mixture of environments with a small set of training data.

Place, publisher, year, edition, pages
IEEE, 2017. article id 8206092
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Robotics
Research subject
Computer Science; Computer Science; Computer Science; Computer Science; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-256309DOI: 10.1109/IROS.2017.8206092Scopus ID: 2-s2.0-85041963716ISBN: 978-1-5386-2682-5 (electronic)OAI: oai:DiVA.org:kth-256309DiVA, id: diva2:1344490
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017; Vancouver; Canada; 24 September 2017 through 28 September 2017
Projects
EU2020 Centauro grant no. 644839
Funder
EU, Horizon 2020, 644839
Note

QC 20190821

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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