Artificial vision and identification for intelligent orientation using a compass

Authors

  • Alejandro Israel Barranco Gutiérrez Applied Science and Advanced Technologies Research Center
  • José de Jesús Medel Juárez Applied Science and Advanced Technologies Research Center

DOI:

https://doi.org/10.17533/udea.redin.14614

Keywords:

computer vision, identification, hand compass, RGB Image

Abstract

A method to determine the orientation of an object relative to Magnetic North using computer vision and identification techniques, by hand compass is presented. This is a necessary condition for intelligent systems with movements rather than the responses of GPS, which only locate objects within a region. Commonly, intelligent systems have vision tools and identification techniques that show their position on the hand compass without relying on a satellite network or external objects that indicate their location. The methodof intelligent guidance is based on image recognition for the red needle of a compass, fi ltering the resulting image, and obtaining the angle direction, that allows finding the orientation of the object.

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References

A. Ollero. Robótica, manipuladores y robots móviles. Ed. Marcombo. Barcelona. 2001. pp. 8-11.

O. Khatib, V. Kumar, D. Rus. “Robust GPS/INS-Aided Localization and Mapping via GPS Bias Estimation”. The 10th International Symposium on Experimental Robotics. Brazil, 2008. pp. 1-2.

W. H. Hayt. Teoría electromagnética. Ed. Mc Graw Hill. Mexico. 2003. pp. 330-340.

R. Hartley. A. Zisserman. Multiple View Geometry in Computer Vision. Ed. Cambridge University Press. Canberra (Australia). 2003. pp. 151-178. DOI: https://doi.org/10.1017/CBO9780511811685

K. Voss, J. L Marroquin, S. J. Gutiérrez, H. Suesse. Análisis de Imágenes de Objetos Tridimensionales. Ed. Polytechnic. Mexico. 2006. pp. 49-74.

J. H. Sossa. Rasgos Descriptores para el reconocimiento de Objetos. Ed. Polytechnic. Mexico. 2006. pp. 10-30.

M. Works. Image Processing Toolbox for use with Matlab User’s Guide. 2003. pp. 7/0- 7/ 11.

N. Otsu. “A threshold selection method from gray level histograms”. IEEE Transactions on Systems. Man, and Cybernetics. Vol. 9. 1979. pp. 62-66. DOI: https://doi.org/10.1109/TSMC.1979.4310076

W. J. Lewandowski, W. Azoubib, J. Klepczynski. “GPS: primary tool for time transfer”. Proceedings of the IEEE. Vol. 87. 1999. pp. 163-172. DOI: https://doi.org/10.1109/5.736348

S. J. Peczalski, A. Kriz, J. Carlson, S. G. Sampson. “Military/civilian mixed-mode Global Positioning System (GPS) receiver (MMGR)”. Aerospace Conference IEEE. Vol. 4. 2004. pp. 2697- 2703.

G. Bullock, J. B. King, T. M. Kennedy, H. L. Berry, E. D. Zanfi no. “Test results and analysis of a low cost core GPS receiver for time transfer applications”. Frequency Control Symposium. Proceedings of the 1997 IEEE International. Vol. 28. 1997. pp. 314-322.

G. A. I. Barranco. Sistema Mecatrónico Controlado Telemáticamente. Ed. Polytechnic. Mexico. 2006. pp. 10-12.

G. A. I. Barranco. J. J. Medel. Visión estereoscópica por computadora. Ed. Polytechnic. México. 2007. pp. 1-4.

B. Espiau. “A New Approach To Visual Servoing In Robotics”. IEEE Transactions in Robotics and Automation. Vol. 18. 1992. pp. 313-326. DOI: https://doi.org/10.1109/70.143350

G. Ritter, J. Wilson. Handbook of Computer Vision Algorithms in Image Algebra. Ed. ITK knowledge. 1996. pp. 125-158.

O. Faugeras, F. Lustman. “Motion and Structure from Motion in a Piecewise Planar Environments”. Proceedings of the 8th WSEAS International Conference on Computer Vision. INRIA France. Vol. 1. 1988. pp. 2-4.

J. Lira. Introducción al tratamiento digital de imágenes. Ed. Polytechnic. Mexico. 2002. pp. 219- 336.

E. Trucco, A. Verri. Introductory Techniques for 3d Computer Vision. Ed. Prentice Hall. Genève (Italy) 1998. pp. 220-292.

P. Gonzáles, J. M. De la Cruz. Visión por computador. Ed. AlfaOmega RaMa. Madrid 2004. pp. 65-294.

D. Poole. Algebra Lineal: Una introducción moderna. Ed. Thompson. México. 2007. pp. 577-590.

G. A. I. Barranco., J. J. Medel., “Digital Camera Calibration Analysis using Perspective Projection Matrix”. Proceedings of the 8th WSEAS International Conference on signal processing, robotics and automation. 2009. pp. 321-325.

Published

2013-02-27

How to Cite

Barranco Gutiérrez, A. I., & Medel Juárez, J. de J. (2013). Artificial vision and identification for intelligent orientation using a compass. Revista Facultad De Ingeniería Universidad De Antioquia, (58), 191–198. https://doi.org/10.17533/udea.redin.14614