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Autonomous Terrain Classification Through Unsupervised Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A key component of autonomous outdoor navigation in unstructured environments is the classification of terrain. Recent development in the area of machine learning show promising results in the task of scene segmentation but are limited by the labels used during their supervised training.

In this work, we present and evaluate a flexible strategy for terrain classification based on three components: A deep convolutional neural network trained on colour, depth and infrared data which provides feature vectors for image segmentation, a set of exchangeable segmentation engines that operate in this feature space and a novel, air pressure based actuator responsible for distinguishing rigid obstacles from those that only appear as such. Through the use of unsupervised learning we eliminate the need for labeled training data and allow our system to adapt to previously unseen terrain classes.

We evaluate the performance of this classification scheme on a mobile robot platform in an environment containing vegetation and trees with a Kinect v2 sensor as low-cost depth camera. Our experiments show that the features generated by our neural network are currently not competitive with state of the art implementations and that our system is not yet ready for real world applications.

Place, publisher, year, edition, pages
2016. , p. 52
Keywords [en]
Terrain Classification, Unsupervised Learning, Robotics, Neural Networks
National Category
Robotics
Identifiers
URN: urn:nbn:se:ltu:diva-60893OAI: oai:DiVA.org:ltu-60893DiVA, id: diva2:1051763
Educational program
Space Engineering, master's level (120 credits)
Supervisors
Examiners
Available from: 2016-12-13 Created: 2016-12-03 Last updated: 2016-12-13Bibliographically approved

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Department of Computer Science, Electrical and Space Engineering
Robotics

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CiteExportLink to record
Permanent link

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Cite
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
  • rtf