hh.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • 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 (Engelska)Ingår i: IROS 2010: the IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Piscataway: IEEE Press, 2010, s. 1183-1189Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Piscataway: IEEE Press, 2010. s. 1183-1189
Serie
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858
Nyckelord [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
Nationell ämneskategori
Robotteknik och automation
Identifikatorer
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
Konferens
IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, TAIWAN, OCT 18-22, 2010
Tillgänglig från: 2013-01-12 Skapad: 2013-01-12 Senast uppdaterad: 2018-03-22Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Iagnemma, Karl

Sök vidare i DiVA

Av författaren/redaktören
Iagnemma, Karl
Robotteknik och automation

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 149 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf