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High-Performance Long Range Obstacle Detection Using Stereo Vision
Daimler RandD, Germany; Goethe University of Frankfurt, Germany.
Daimler RandD, Germany.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University of Frankfurt, Germany.
2015 (English)In: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, 1308-1313 p.Conference paper, Published paper (Refereed)
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Text
Abstract [en]

Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The approach is applicable to both static and moving obstacles and pushes the limits of detection performance as well as localization accuracy. The proposed detection algorithm is based on sound statistical tests using local geometric criteria which implicitly consider non-flat ground surfaces. To achieve maximum performance, it operates directly on image data instead of precomputed stereo disparity maps. A careful experimental evaluation on several datasets shows excellent detection performance and localization accuracy up to very large distances, even for small obstacles. We demonstrate a parallel implementation of the proposed system on a GPU that executes at real-time speeds.

Place, publisher, year, edition, pages
IEEE , 2015. 1308-1313 p.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-127068DOI: 10.1109/IROS.2015.7353537ISI: 000371885401068ISBN: 978-1-4799-9994-1 (print)OAI: oai:DiVA.org:liu-127068DiVA: diva2:919357
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Available from: 2016-04-13 Created: 2016-04-13 Last updated: 2016-04-13

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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
  • oxford
  • 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