Landmark Finding Algorithms for Indoor Autonomous Mobile Robot Localization

L. TóthS. PaulovičZ. PalkováL. Vacho


No 4/2015, December
pp. 189-197

Tóth, L., Paulovič, S., Palková, Z. and Vacho, L. (2015) “Landmark Finding Algorithms for Indoor Autonomous Mobile Robot Localization”, AGRIS on-line Papers in Economics and Informatics, Vol. 7, No. 4, pp.189 - 197, ISSN 1804-1930.

Abstract

This contribution is oriented to ways of computer vision algorithms for mobile robot localization in internal and external agricultural environment. The main aim of this work was to design, create, verify and evaluate speed and functionality of computer vision localization algorithm. An input colour camera data and depth data were captured by MS® Kinect sensor that was mounted on 6-wheel-drive mobile robot chassis. The design of the localization algorithm was focused to the most significant blobs and points (landmarks) on the colour picture. Actual coordinates of autonomous mobile robot were calculated out from measured distances (depth sensor) and calculated angles (RGB camera) with respect to landmark points. Time measurement script was used to compare the speed of landmark finding algorithm for localization in case of one and more landmarks on picture. The main source code was written in MS Visual studio C# programming language with Microsoft.Kinect.1.7.dll on Windows based PC. Algorithms described in this article were created for a future development of an autonomous agronomical mobile robot localization and control.

Keywords

Computer vision, localization, MS® Kinect, algorithm, agronomical mobile robot.

References

  1. Abdel-Hafez, M. F., Kim, D. J., Lee, E., Chun, S., Lee, Y. J., Kang, T., Sung, S. Performance improvement of the wald test for GPS RTK with the assistance of INS. International Journal of Control, Automation and Systems. 2008, Vol. 6, No. 4, p. 534-543. ISSN 1598-6446.
  2. Cviklovič, V. Alternative ways of navigation of autonomous mobile robots in agriculture. SUA in Nitra. 2011. Dissertation work.
  3. Cviklovič, V., Hrubý, D., Olejár, M., Lukáč, O. Comparison of numerical integration methods in strapdown inertial navigation algorithm. In Research in Agricultural Engineering. 2011, Vol. 57, special iss., p. 30-34. ISSN 1212-9151. DOI 10.17221/58/2010-RAE.
  4. Cviklovič, V., Hrubý, D., Olejár, M., Priatková, L. Gyroscope calibration with the method of simulated identification. Research in Agricultural Engineering. 2013, Vol. 59, spec iss., p. 22-26. ISSN 1212-9151. DOI 10.17221/47/2012-RAE.
  5. Davison, A. J., Reid, I. D., Molton, N. D., Stasse, O. MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007, Vol. 29, No. 6, p. 1052-1067 ISSN 0162-8828. DOI 10.1109/TPAMI.2007.1049.
  6. Duchoň, F., Králik, M., Babinec, A., Hubinský, P. Simple image processing algorithms for robot navigation in unknown environment. In Applied Mechanics and Materials. 2014, Vol. 613, p. 66-75. ISSN 1660-9336. DOI 10.4028/www.scientific.net/AMM.613.66.
  7. Duchoň, F. Lokalizácia a navigácia mobilných robotov do vnútorného prostredia. 1st ed., Bratislava: Slovenská technická univerzita v Bratislava nakladateľstvo STU. 2012, p. 162. ISBN 978-80-227-3646-6.
  8. Duda, R. O., Hart, P. E. Use of the Hough transformation to detect lines and curves in pictures. Comm. Assoc. Comput. Mach. 1972, Vol. 15, p. 11-15. DOI 10.1145/361237.361242.
  9. Eade, E., Drummond, T. Scalable monocular SLAM. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, Vol. 1, art. No. 1640794, p. 469-476. ISSN 1063-6919.
  10. Hrubý, D., Amrich, M. Automatic fuzzy logic control of vehicle motion. In Acta technologica agriculturae. 2007, Vol. 10, No. 1, p. 14-17. ISSN 1335-2555.
  11. Hu, G., Huang, S., Zhao, L., Alempijevic, A., Dissanayake, G. A robust RGB-D SLAM algorithm. Proceedings of the IEEE International Conference on Intelligent Robots and Systems. 2012, art. No. 6386103, p. 1714-1719. ISSN 2153-0858.
  12. Ioannou, D., Huda, W., Laine, A. F. Circle recognition through a 2D Hough transform and radius histogramming. In Image Vision Comput. 1999, Vol. 17, p. 15–26. ISSN 0262-8856. DOI 10.1016/S0262-8856(98)00090-0.
  13. Khan, F. S., Anwer, R. M., Weijer, J., Bagdanov, A. D., Vanrell, M., Lopez, A. M. Color attributes for object detection. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR). 2012, art. No. 6248068, p. 3306-3313. ISSN 1063-6919.
  14. Kim, H., Lee, D., Oh, T., Choi, H.-T., Myung, H. A probabilistic feature map-based localization system using a monocular camera. Sensors (Switzerland). 2015, Vol. 15, No. 9, p. 21636-21659. ISSN 1424-8220. DOI 10.3390/s150921636.
  15. Ohya, A., Kosaka, A., Kak, A. Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. Proceedings of the IEEE Transactions on Robotics and Automation. 1998, Vol. 14, No. 6, p. 969-978, ISSN 1042 296X. DOI 10.1109/70.736780.
  16. Royer, E., Lhuillier, M., Dhome, M., Lavest, J.-M. Monocular vision for mobile robot localization and autonomous navigation. In International Journal of Computer Vision. 2007, Vol. 74, No. 3, p. 237-260. ISSN 0920-5691. DOI 10.1007/s11263-006-0023-y.
  17. Son, J., Kim, S., Sohn, K. A multi-vision sensor-based fast localization system with image matching for challenging outdoor environments. In Expert Systems with Applications. 2015, Vol. 42, No. 22, p. 8830-8839. ISSN 0957-4174. DOI 10.1016/j.eswa.2015.07.035.
  18. Vaz, M., Ventura, R. Real-time ground-plane based mobile localization using Depth Camera in Real Scenarios. In Journal of Intelligent and Robotic Systems: Theory and Applications. 2015, p. 12. ISSN 0921-0296. DOI 10.1109/ICARSC.2014.6849784.
  19. Wang, J., Zha, H., Copolla, R. Coarse-to-Fine vision-based localization by indexing scale-invariant features. Proceedings of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics. 2006, Vol. 36, No. 2, p. 413-422. ISSN 1083-4419. DOI 10.1109/TSMCB.2005.859085.
  20. Wolf, J., Burgard, W., Burkhardt, H. Robust vision-based localization for mobile robots using an image retrieval system based on invariant features. Proceedings of the IEEE International Conference on Robotics and Automation ICRA '02. 2002, Vol.1, p. 359-365. ISSN 1050-4729.
  21. Yuen, H., Princen, J., Illingworht, J., Kittler, J. Comparative study of Hough Transform methods for circle finding. Image and Vision Computing. 1990, Vol. 8, No. 1, p. 71-77. ISSN 0262-8856. DOI 10.1016/0262-8856(90)90059-E.

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