Evaluation of the SIFT Object Recognition Method in Mobile Robots

Authors:
Arnau Ramisa;Shrihari Vasudevan;David Aldavert;Ricardo Toledo;Ramon Lopez de Mantaras
Affiliations:
Artificial Intelligence Research Institute (IIIA-CSIC), Spain;Australian Centre for Field Robotics (ACFR), Australia;Computer Vision Center (CVC), Spain;Computer Vision Center (CVC), Spain;Australian Centre for Field Robotics (ACFR), Australia
Venue:
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Year:
2009

Citing 6
Cited 0

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Efficient Shape Matching Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cognitive maps for mobile robots-an object based approach

Robotics and Autonomous Systems
A tale of two object recognition methods for mobile robots

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Point matching as a classification problem for fast and robust object pose estimation

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Generic object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Towards this aim, the contribution of this paper is an evaluation of the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. The method presented robustness to the typical problems of images acquired in the robotics domain, but its good performance was limited mainly to well-textured objects.