Semi-supervised logo-based indoor localization using smartphone cameras

In this paper, we propose a homography-aware semi-supervised formulation for the logo-based indoor localization problem using smartphone cameras. Our method labels unmatched feature points detected inside the logo parts of query images with their estimated 3D coordinates. The 3D coordinates are computed using the homography estimated from the matched features. We demonstrate the accuracy improvement and lower localization error variance resulted from our semi-supervised approach via experiments in an indoor scenario.

[1]  Zhen Zhang,et al.  Pose Estimation Based on Four Coplanar Point Correspondences , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[2]  Daisuke Chugo,et al.  Camera-based localization for indoor service robots using pictographs , 2011, 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

[3]  Chadly Marouane,et al.  Indoor positioning using smartphone camera , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[4]  Daisuke Chugo,et al.  Localization for indoor service robot by using local-features of image , 2009, 2009 ICCAS-SICE.

[5]  Hongbin Zha,et al.  Coarse-to-fine vision-based localization by indexing scale-Invariant features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Liviu Iftode,et al.  Indoor Localization Using Camera Phones , 2006, Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement).

[7]  Vincent Lepetit,et al.  Accurate Non-Iterative O(n) Solution to the PnP Problem , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Shahrokh Valaee,et al.  Indoor positioning and distance-aware graph-based semi-supervised learning method , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[9]  M. J. Gilmartin.  INTRODUCTION TO AUTONOMOUS MOBILE ROBOTS, by Roland Siegwart and Illah R. Nourbakhsh, MIT Press, 2004, xiii+321 pp., ISBN 0-262-19502-X. (Hardback, £27.95) , 2005 .

[10]  Radu Horaud,et al.  An analytic solution for the perspective 4-point problem , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[12]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .