Indoor Precise Positioning Technology for Vehicles Using Floor Marks

플로어 마크를 이용한 차량용 실내 정밀 측위 기술

  • Received : 2015.06.29
  • Accepted : 2015.09.11
  • Published : 2015.10.31


A variety of studies for indoor positioning are now being in progress due to the limit of GPS that becomes obsolete in the room. However, most of them are based on private wireless networks and the situation is difficult to commercialize them since they are expensive in terms of installation and maintenance costs, non-real-time, and not accurate. This paper applies the mark recognition algorithm used in existing augmented reality applications to the indoor vehicle positioning application. It installs floor marks on the ground, performs the perspective transformation on it and decodes the internal data of the mark and, as a result, it obtains an absolute coordinate. Through the geometric analysis, it obtains current position (relative coordinates) of a vehicle away from the mark and the heading direction of the vehicle. The experiment results show that when installing the marks every 5 meter, an error under about 30 cm occurred. In addition, it is also shown that the mark recognition rate is 43.2% of 20 frames per second at the vehicle speed of 20km/h. Thus, it is thought that this idea is commercially valuable.


ITS;floor mark;image processing;indoor positioning;augmented reality


  1. Hui Liu, "Survey of Wireless Indoor Positioning Techniques and Systems," IEEE Transaction on system, man, and cybernetics - part C: applications and reviews, VOL. 37, no. 6, november 2007.
  2. Yanying Gu, "A Survey of Indoor Positioning Systems for Wireless Personal Networks", IEEE communication surveys & tutorials, vol. 11, no. 1, first quarter 2009.
  3. T Moons, L Van Gool, M Proesmans, and E. Pauwels, “Affine reconstruction from perspective image pairs with a relative object-camera translation in between,”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 18, NO. 1, JANUARY 1996.
  4. L. D. Stefano and A. Bulgarelli, "A simple and efficient connected components labeling algorithm", in Proceedings of International Conference on Image Analysis and Processing, 1999, pp322-327.
  5. Ramer, Urs. "An iterative procedure for the polygonal approximation of plane curves." Computer graphics and image processing 1.3 (1972): 244-256.
  6. Douglas, David H., and Thomas K. Peucker. "Algorithms for the reduction of the number of points required to represent a digitized line or its caricature." Cartographica: The International Journal for Geographic Information and Geovisualization 10.2 (1973): 112-122.
  7. Kongyang Chen, Guang Tan, “Modeling and Improving the Energy Performance of GPS Receivers for Mobile Applications”, Shenzhen Institute of Advanced Integ- ration Technology(SIAT), Chinese Academy of Sciences, Mar 2015.
  8. B. Froba, and A. Ernst, "Face detection with the modified census transform", in Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91-96, 17-19 May 2004.
  9. Lee, Jae Yeong, and Wonpil Yu. "Robust self-localization of ground vehicles using artificial landmark." Ubiquitous Robots and Ambient Intelligence (URAI), 2014 11th International Conference on. IEEE, 2014.


Grant : 스마트교통특화전문인력양성사업단