Robot Localization and Object Detection with Fish-Eye Vision System and Sensors

G.Vara Prasad*, Shoba Bindu C**
* PG Student, Department of Computer Science & Engineering, JNTUA College of Engineering, Ananthapuramu, India.
** Associate Professor, Department of Computer Science & Engineering, JNTUA College of Engineering, Ananthapuramu, India.
Periodicity:September - November'2015
DOI : https://doi.org/10.26634/jpr.2.3.3758

Abstract

This paper addresses the problem of designing an autonomous robot for the purpose of navigating in the sensitive areas, keeping focus on localization of the robot. If a robot doesn't know its current location, it is very difficult to determine its further activities. Thus, localization plays a vital role in building an efficient mobile robot. This paper mainly focuses on design and implementation of OI-ROBOT (Object Identification Robot) which mainly comprises of fish eye lens camera to obtain Omni-directional vision, Sensors, to identify the position of the robot and an embedded micro controller that takes charge in target recognition and distortion rectification. The experimental results demonstrate the navigation and selflocalization of the mobile robot. This Robot also helps in fire detection and can be easily available through an Andriod phone or Internet.

Keywords

Fish-Eye Vision System, Landmark-Based Localization, Mobile Robot, Robotic Vision, Android

How to Cite this Article?

Prasad, G. V., and Bindu, C. S. (2015). Robot Localization and Object Detection with Fish-Eye Vision System and Sensors. i-manager’s Journal on Pattern Recognition, 2(3), 16-23. https://doi.org/10.26634/jpr.2.3.3758

References

[1]. Seung-Beom Han, Jong-Hwan Kim and Hyun Myung, (2013). "Landmark-Based Particle Localization Algorithm for Mobile Robots with a Fish-Eye Vision System". IEEE/ASME Transactions on Mechatronics, Vol.18, No. 6, pp. 1745- 1756.
[2]. S. Urban, J. Leitloff, S. Wursthorn and S. Hinz, (2013). “Self-localization of A Multi-fisheye Camera Based Augmented Reality System In Textureless 3D Building Models". The ISPRS Workshop on Image Sequence Analysis , Vol. 2, No. 3, pp. 43-48.
[3]. Jen-Shiun Chiang, Chih-Hsien Hsia, and Hung-Wei Hsu, (2013). “A Stereo Vision-Based Self-Localization System". IEEE Sensors Journal, Vol. 13, No. 5, pp. 1677- 1689.
[4]. Byoung-Suk Choi, Joon-Woo Lee, Ju-Jang Lee, and Kyoung-Taik Park, (2011). "A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion", IEEE Transactions on Industrial Electronics, Vol. 58, No. 6, pp. 2226-2235.
[5]. Emanuele Menegatti, Alberto Pretto, Alberto Scarpa, and Enrico Pagello, (2006). "Omnidirectional Vision Scan Matching for Robot Localization in Dynamic Environments". IEEE Transactions on Robotics, Vol. 22, No. 3, pp. 523- 535.
[6]. J. Huang, S. Farritor, A. Qadi, and S. Goddard, (2006). "Localization and follow-the-leader control of a heterogeneous group of mobile robots". IEEE/ASME Trans. Mechatronics, Vol. 11, No. 2, pp. 205–215.
[7]. F. Thomas and L. Ros, (2005). "Revisiting trilateration for robot localization". IEEE Trans. Robot., Vol. 21, No. 1, pp. 93–101.
[8]. J. J. Leonard and H. F. Durrant-Whyte, (1991). "Mobile robot localization by tracking geometric beacons". IEEE Transactions on Robotics and Automation, Vol. 7, No. 3, pp. 376-382.
[9]. S. Thrun (1998). "Bayesian landmark learning for mobile robot localization". Machine Learning, Vol. 33, No. 1, pp. 41-76.
[10]. S. Thrun, (1998). "Learning maps for indoor mobile robot navigation". Artificial Intelligence, Vol. 99, No.1, pp. 21-71.
[11]. A. Elfes, (1987). "Sonar-based real world mapping and navigation," IEEE Journal of Robotics and Automation, RA, Vol. 3, No. 3, pp. 249-265.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.