hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines
Intelligent Vehicle Research Center, Beijing Institute of Technology, China.
Robotic Mobility Group, MIT, United States.
2010 (English)In: IROS 2010: the IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Piscataway: IEEE Press, 2010, p. 1183-1189Conference paper, Published paper (Refereed)
Abstract [en]

Road detection is a crucial problem in the application of autonomous vehicle and on-road mobile robot. Most of the recent methods only achieve reliable results in some particular well-arranged environments. In this paper, we describe a road detection algorithm for front-view monocular camera using road probabilistic distribution model (RPDM) and online learning method. The primary contribution of this paper is that the combination of dynamical RPDM and Fuzzy Support Vector Machines (FSVMs) makes the algorithm being capable of self-supervised learning and optimized learning from the inheritance of previous result. The secondary contribution of this paper is that the proposed algorithm uses road geometrical assumption to extract assumption based misclassified points and retrains itself online which makes it easier to find potential misclassified points. Those points take an important role in online retraining the classifier which makes the algorithm adaptive to environment changing.

Place, publisher, year, edition, pages
Piscataway: IEEE Press, 2010. p. 1183-1189
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858
Keyword [en]
fuzzy set theory, geometry, learning (artificial intelligence), mobile robots, object detection, probability, robot vision, support vector machines, autonomous vehicle, front-view monocular camera, fuzzy support vector machines, on-road mobile robot, online learning method, road probabilistic distribution model, self-supervised learning method, unstructured road detection algorithm
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20822DOI: 10.1109/IROS.2010.5650300ISI: 000287672004117Scopus ID: 2-s2.0-78651512763ISBN: 978-1-4244-6675-7 OAI: oai:DiVA.org:hh-20822DiVA, id: diva2:586711
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, TAIWAN, OCT 18-22, 2010
Available from: 2013-01-12 Created: 2013-01-12 Last updated: 2018-03-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Iagnemma, Karl

Search in DiVA

By author/editor
Iagnemma, Karl
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 76 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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