Authors:
M. Shuja Ahmed
;
Reza Saatchi
and
Fabio Caparrelli
Affiliation:
Sheffield Hallam University, United Kingdom
Keyword(s):
Object Recognition, SURF, Harris Features, Multi-robotics.
Related
Ontology
Subjects/Areas/Topics:
Distributed Intelligent Agents
;
Mobile and Pervasive Computing
;
Mobile Computing
;
Pervasive Embedded Networks
;
Telecommunications
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.
(More)