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Learning Deep Generative Spatial Models for Mobile Robots
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-1396-0102
University of Washington, Seattle.
2017 (English)In: Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’17), IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unlike traditional approaches that combine engineered models for low-level features, geometry, and semantics, our approach leverages recent advances in Sum-Product Networks (SPNs) and deep learning to learn a single, universal model of the robot's spatial environment. Our model is fully probabilistic and generative, and represents a joint distribution over spatial information ranging from low-level geometry to semantic interpretations. Once learned, it is capable of solving a wide range of tasks: from semantic classification of places, uncertainty estimation, and novelty detection, to generation of place appearances based on semantic information and prediction of missing data in partial observations. Experiments on laser-range data from a mobile robot show that the proposed universal model obtains performance superior to state-of-the-art models fine-tuned to one specific task, such as Generative Adversarial Networks (GANs) or SVMs.

Place, publisher, year, edition, pages
IEEE, 2017.
Series
IEEE/RSJ International Conference on Intelligent Robots and Systems
Keyword [en]
Robotics, Machine Learning, Deep Learning, Sum-Product Networks
National Category
Robotics Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-214781OAI: oai:DiVA.org:kth-214781DiVA: diva2:1143214
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’17), 24 Sep - 28 Sep 2017, Vancouver, BC, Canada
Projects
VR Project 2012-4907 SKAEENet
Funder
Swedish Research Council, 2012-4907
Note

QCR 20170921

Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
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