Image Features Comparison-Based Position Estimation Method Using a Camera Sensor

Authors: Jinseon Song, Yongwan Park

Abstract:

In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction.

Keywords: Positioning, Distance, Camera, Features, SURF (Speed-Up Robust Features), Database, Estimation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339193

References:

[1] N. Karlsson, E.D. Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, M.E. Munich, “The vSLAM Algorithm for Robust Localization and Mapping,” Proc. IEEE Int’l Conference Robotics and Automation, 2005.
[2] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, Canada, 2002.
[3] Yanying Gu, Anthony Lo, Ignas Niemegeers “A Survey of indoor positioning systems for wireless personal networks” Proc. IEEE communication surveys & tutorials 2009.
[4] K. Kaemarungsi, “Efficient design of indoor positioning systems based on position fingerprint” International conference on wireless networks, communication and mobile computing, 13-16 June 2005 vol. 1, pp. 181-186.
[5] J. B. Kim, H. S. Jun, "Vision-Based Position Positioning using Augmented Reality for Indoor Navigation", IEEE Transactions on Comsumer Electronics, vol. 54, No. 3, August 2008.
[6] A. Baumberg. “Reliable feature matching across widely separated views” In CVPR, 2000, pp. 774 – 781.
[7] M. Brown, D. Lowe, "Invariant features from interest point groups" In Proceedings of the 13th British Machine Vision Conference(BMVC), 2002, pp. 253-262.
[8] Hui Liu, Houshang Darabi, Pat Banerjee, Jing Liu, "Survey of wireless indoor positioning techniques and systems", IEEE Transactions on System, Man, and Cybernetics—Part C: Applications and reviews, vol. 37, NO. 6, November 2007.
[9] H. Bay. T. Tuytelaars, L. Van Gool, “SURF: Speeded-Up Robust Features", in ECCV, 2006, pp. 404-417.
[10] D. Bradley, G. Roth, “Adaptive Thresholding Using the Integral Image”, Jonrnal of Graphics, GPU, and Game Tools 2007, vol. 12, Issue 2, pp. 13-21.
[11] J. Wolf, W. Burgard, and H. Burkhardt. “Robust vision-based localization for mobile robots using an image retrievel system based on invariant features” In Proc. of Int. Conf. on Robotics and Automation (ICRA), 2002.