An Efficient Approach to Object Recognition for Mobile Robots

M. Shuja Ahmed, Reza Saatchi, Fabio Caparrelli

2013

Abstract

In robotics, the object recognition approaches developed so far have proved very valuable, but their high memory and processing requirements make them suitable only for robots with high processing capability or for offline processing. When it comes to small size robots, these approaches are not effective and lightweight vision processing is adopted which causes a big drop in recognition performance. In this research, a computationally expensive, but efficient appearance-based object recognition approach is considered and tested on a small robotic platform which has limited memory and processing resources. Rather than processing the high resolution images, all the times, to perform recognition, a novel idea of switching between high and low resolutions, based on the “distance to object” is adopted. It is also shown that much of the computation time can be saved by identifying the irrelevant information in the images and avoid processing them with computationally expensive approaches. This helps to bridge the gap between the computationally expensive approaches and embedded platform with limited processing resources.

References

  1. Ahmed, M., Saatchi, R., and Caparrelli, F. (2012a). Vision based object recognition and localisation by a wireless connected distributed robotic systems. In Electronic Letters on Computer Vision and Image Analysis Vol. 11, No. 1, Pages 54-67.
  2. Ahmed, M., Saatchi, R., and Caparrelli, F. (2012b). Vision based obstacle avoidance and odometery for swarms of small size robots. In Proceedings of 2nd International Conference on Pervasive and Embedded Computing and Communication Systems, Pages 115-122.
  3. Asanza, D. and Wirnitzer, B. (2010). Improving feature based object recognition in service robotics by disparity map based segmentation. In International ConferChrysanthakopoulos, G. and Shani, G. (2010). Augmenting appearance-based localization and navigation using belief update. In Proceedings of AAMAS 2010, Pages 559-566.
  4. Cummins, M. and Newman, P. (2010). Fab-map: Appearance-based place recognition and mapping using a learned visual vocabulary model. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), Vol.27 No.6 Pages 647-665.
  5. Dillmann, R., Welke, K., and Azad, P. (2007). Fast and robust feature-based recognition of multiple objects. In 6th IEEE/RAS International Conference on Humanoid Robots, Pages 264-269.
  6. Evans, C. (2009). Notes on the opensurf library. In OpenSurf, Page 25, URL: www.cs.bris.ac.uk/Publications/PublishedPapers/2000970.pdf.
  7. Juan, L. and Gwun, O. (2009). A comparison of sift, pca-sift and surf. In International Journal of Image Processing (IJIP), Vol.3, No.4, Pages 143-152.
  8. Kaehler, A. and Bradski, G. (2008.). Computer vision with the opencv library. In Learning OpenCV, Page 576.
  9. Katz, D., Lukasiak, T., and Gentile, R. (2005). Enhance processor performance in open-source applications. In Analog Dialogue, Vol.39.
  10. Krose, B., Vlassis, N., Bunschoten, R., and Motomura, Y. (2001). A probabilistic model for appearance-based robot localization. In In First European Symposium on Ambience Intelligence (EUSAI), Pages 264-274.
  11. Lowe, D. (2004). Distinctive image features from scaleinvariant keypoints. In International Journal of Computer Vision, Vol. 60. No.2 Pages 91-110.
  12. Nayerlaan, J. and Goedeme, T. (2008). Traffic sign recognition with constellations of visual words. In International conference on informatics in control, automation and robotics - ICINCO.
  13. Ramos, F., Tardos, J., Cadena, C., Lopez, D., and Neira, J. (2010). Robust place recognition with stereo cameras. In International Conference on Intelligent Robots and Systems (IROS), IEEE, Pages 5182-5189.
  14. Ricardo, C. and Pellegrino, S. (2010). An experimental evaluation of algorithms for aerial image matching. In IWSSIP 17th International Conference on Systems, Signals and Image Processing.
  15. Siegwart, R., Sabatta, D., and Scaramuzza, D. (2010). Improved appearance-based matching in similar and dynamic environments using a vocabulary tree. In IEEE International Conference on Robotics and Automation(ICRA), Pages 1008-1013.
  16. Tuytelaars, T., Bay, H., and Gool, L. (2008). Surf speeded up robust features. In Computer Vision and Image Understanding (CVIU), Pages 110-346.
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Paper Citation


in Harvard Style

Ahmed M., Saatchi R. and Caparrelli F. (2013). An Efficient Approach to Object Recognition for Mobile Robots . In Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8565-43-3, pages 60-65. DOI: 10.5220/0004314800600065


in Bibtex Style

@conference{peccs13,
author={M. Shuja Ahmed and Reza Saatchi and Fabio Caparrelli},
title={An Efficient Approach to Object Recognition for Mobile Robots},
booktitle={Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2013},
pages={60-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004314800600065},
isbn={978-989-8565-43-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - An Efficient Approach to Object Recognition for Mobile Robots
SN - 978-989-8565-43-3
AU - Ahmed M.
AU - Saatchi R.
AU - Caparrelli F.
PY - 2013
SP - 60
EP - 65
DO - 10.5220/0004314800600065