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TrackSense: Infrastructure Free Precise Indoor Positioning Using Projected Patterns

  • Moritz Köhler
  • Shwetak N. Patel
  • Jay W. Summet
  • Erich P. Stuntebeck
  • Gregory D. Abowd
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4480)

Abstract

While commercial solutions for precise indoor positioning exist, they are costly and require installation of additional infrastructure, which limits opportunities for widespread adoption. Inspired by robotics techniques of Simultaneous Localization and Mapping (SLAM) and computer vision approaches using structured light patterns, we propose a self-contained solution to precise indoor positioning that requires no additional environmental infrastructure. Evaluation of our prototype, called TrackSense, indicates that such a system can deliver up to 4 cm accuracy with 3 cm precision in rooms up to five meters squared, as well as 2 degree accuracy and 1 degree precision on orientation. We explain the design and performance characteristics of our prototype and demonstrate a feasible miniaturization that supports applications that require a single device localizing itself in a space. We also discuss extensions to locate multiple devices and limitations of this approach.

Keywords

Augmented Reality Stereo Vision Epipolar Line Projected Pattern Grid Projector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Active Bat. The BAT Ultrasonic Location System (2006), http://www.uk.research.att.com/bat/
  2. 2.
    Bahl, P., Padmanabhan, V.: RADAR: An In-Building RF-Based User Location and Tracking System. In: The Proc. of IEEE Infocom 2000, pp. 775–784. IEEE Press, Los Alamitos (2000)Google Scholar
  3. 3.
    Bouguet, J.Y., Perona, P.: 3D photography on your desk. In: ICCV’98, pp. 43–50 (1998)Google Scholar
  4. 4.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6) (1986)Google Scholar
  5. 5.
    Cao, X., Balakrishnan, R.: Interacting with dynamically defined information spaces using a handheld projector and a pen. In: Proc. of UIST 2006, pp. 225–234 (2006)Google Scholar
  6. 6.
    Cobzas, D., Sturm, P.: 3D SSD Tracking with Estimated 3D Planes. In the proc. In: Proc. of 2nd Canadian Conference on Computer and Robot Vision (CRV2005), pp. 129–134 (2005)Google Scholar
  7. 7.
    Davison, A., Cid, Y., Kita, N.: Real-Time 3D SLAM with Wide-Angle Vision. In: Proc. of IFAC Symposium on Intelligent Autonomous Vehicles (2004)Google Scholar
  8. 8.
    Dellaert, F., Tariq, S.: A Multi-Camera Pose Tracker for Assisting the Visually Impaired. In: 1st IEEE Workshop on Computer Vision Applications for the Visually Impaired (2005)Google Scholar
  9. 9.
    Dissanayake, M.W.M.G., et al.: A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)CrossRefGoogle Scholar
  10. 10.
    Fischler, M., Bolles, R.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  12. 12.
    Harle, R.K., Hopper, A.: Cluster Tagging: Robust Fiducial Tracking for Smart Environments. In: 2nd International Workshop on Location- and Context-Awareness, May 2006, pp. 14–29 (2006)Google Scholar
  13. 13.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge Press, Cambridge (2003)Google Scholar
  14. 14.
    Hightower, J., Borriello, G.: A Survey and Taxonomy of Location Systems for Ubiquitous Computing. University of Washington Tech. Report CSC-01-08-03 (2001)Google Scholar
  15. 15.
    Borriello, G., et al.: Place Lab: Device Positioning Using Radio Beacons in the Wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Kuhn, H.W.: The Hungarian Method for the assignment problem. Naval Research Logistic Quarterly (2), 83–97 (1955)Google Scholar
  17. 17.
    Lourakis, M.A., Argyros, A.A.: Vision-Based Camera Motion Recovery for Augmented Reality. In: Proc. of the Computer Graphics International (CGI 2004), pp. 569–576 (2004)Google Scholar
  18. 18.
    Liu, Y., Thrun, S.: Results for outdoor-SLAM using sparse extended information filters. In: Proc. of the IEEE International Conference on Robotics and Automation, pp. 1227–1233 (2003)Google Scholar
  19. 19.
    Madhavapeddy, A., Tse, A.: A Study of Bluetooth Propagation Using Accurate Indoor Location Mapping. In: Beigl, M., et al. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 105–122. Springer, Heidelberg (2005)Google Scholar
  20. 20.
    Munkres, J.: Algorithms for the Assignment and Transportation Problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32–38 (1957)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    NorthStar. Evolution Robitcs (2007), http://www.evolution.com/products/northstar/
  22. 22.
    de Lara, E., et al.: Accurate GSM Indoor Localization. In: Beigl, M., et al. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 141–158. Springer, Heidelberg (2005)Google Scholar
  23. 23.
    Rekimoto, J., Abowd, G.D., Patel, S.N.: iCam: Precise at-a-Distance Interaction in the Physical Environment. In: Fishkin, K.P., et al. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 272–287. Springer, Heidelberg (2006)Google Scholar
  24. 24.
    Patel, S.N., Truong, K.N., Abowd, G.D.: PowerLine Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 441–458. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The Cricket Location-Support System. In: Proc. of Mobicom, Boston, MA (August 2000)Google Scholar
  26. 26.
    Rekimoto, J., Ayatsuka, Y.: CyberCode: Designing Augmented Reality Environments with Visual Tags. In: Proc. of DARE 2000, Elsinore, Denmark, pp. 1–10 (2000)Google Scholar
  27. 27.
    Rachmielowski, A., Cobzas, D., Jagersand, M.: Robust SSD tracking with incremental 3D structure estimation. In: Proc. of Canadian Conference on Computer and Robot Vision (CRV) (2006)Google Scholar
  28. 28.
    Rekimoto, J., Katashi, N.: The World through the Computer: Computer Augmented Interaction with Real World Environments. In: Proc. of UIST 1995, Pittsburgh, PA, pp. 29–36 (1995)Google Scholar
  29. 29.
    Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: CVPR 2003, vol. 1, Madison, WI, June 2003, pp. 195–202 (2003)Google Scholar
  30. 30.
    Se, S., Lowe, D., Little, J.: Vision-Based Mobile Robot Localization and Mapping Using Scale-Invariant Features. In: Proc. of ICRA (2001)Google Scholar
  31. 31.
    Sukthankar, R., Stockton, R., Mullin, M.: Smarter Presentations: Exploiting Homography in Camera-Projector Systems. In: Proc. of ICCV (2001)Google Scholar
  32. 32.
    Sukthankar, R., Stockton, R., Mullin, M.: Automatic Keystone Correction for Camera-assisted Presentation Interfaces. In: Proc. of International Conference on Multimodal Interfaces (2000)Google Scholar
  33. 33.
    Tariq, S., Dellaert, F.: A Multi-Camera 6-DOF Pose Tracker. In: IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), pp. 296–297 (2004)Google Scholar
  34. 34.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  35. 35.
    Ubisense (2006), http://www.ubisense.net
  36. 36.
  37. 37.
    Want, R., et al.: The active badge location system. ACM Transactions on Information Systems 10, 91–102 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Moritz Köhler
    • 1
  • Shwetak N. Patel
    • 2
  • Jay W. Summet
    • 2
  • Erich P. Stuntebeck
    • 2
  • Gregory D. Abowd
    • 2
  1. 1.Institute for Pervasive Computing, Department of Computer Science, ETH Zurich, 8092 ZurichSwitzerland
  2. 2.College of Computing & GVU Center, Georgia Institute of Technology, 85 5th Street NW, Atlanta GA 30332-0280USA

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