An experiment of a 3D real-time robust visual odometry for intelligent vehicles

Vision systems are nowadays very promising for many on-board vehicles perception functionalities, like obstacles detection/recognition and ego-localization. In this paper, the authors present a 3D visual odometric method that uses a stereo-vision system to estimate the 3D ego-motion of a vehicle in outdoor road conditions. In order to run in real-time, the studied technique is sparse meaning that it makes use of feature points that are tracked during several frames. A robust scheme is also employed to reject outliers that are detected on moving objects of the environment. Moreover, efforts have been spent on the realtime implementation of the method. In this article, the authors describe the key stages of the method: features extraction and tracking, quadrifocal constraints, optimization solver and robustification. Real experiments are reported to compare the performance of this approach with Global Positioning System (GPS) data and 2D-wheel-based odometry.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: 6p
  • Monograph Title: 12th International IEEE Conference on Intelligent Transportation Systems (ITSC 2009)

Subject/Index Terms

Filing Info

  • Accession Number: 01573811
  • Record Type: Publication
  • ISBN: 9781424455195
  • Files: TRIS
  • Created Date: Aug 14 2015 6:07PM