Robust and efficient feature tracking for indoor navigation

  • Authors:

  • Ranga Rodrigo;Mehrnaz Zouqi;Zhenhe Chen;Jagath Samarabandu

  • Affiliations:

  • Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri Lanka;Department of Electrical and Computer Engineering, The University of Western Ontario, London, ON, Canada;TVWorks, London, ON, Canada;Department of Electrical and Computer Engineering, The University of Western Ontario, London, ON, Canada

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2009

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Abstract

Robust feature tracking is a requirement for many computer vision tasks such as indoor robot navigation. However, indoor scenes are characterized by poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of nondistinctive features by several planar homographies which are strategically computed using distinctive features. We experimentally show the need for multiple homographies and propose an illumination-invariant local-optimization scheme for motion refinement. The use of a large number of nondistinctive features within the constraints imposed by planar homographies allows us to gain robustness. Also, the lesser computation cost in estimating these nondistinctive features helps to maintain the efficiency of the proposed method. Our local-optimization scheme produces subpixel accurate featuremotion. As a result, we are able to achieve robust and accurate feature tracking.