This paper addresses object perception applied to mobile robotics. Being able to perceive semantically meaningful objects in unstructured environments is a key capability in order to make robots suitable to perform high-level tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.
@article{arxiv.1102.0454,
title = {Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot},
author = {Arnau Ramisa and David Aldavert and Shrihari Vasudevan and Ricardo Toledo and Ramon Lopez de Mantaras},
journal= {arXiv preprint arXiv:1102.0454},
year = {2015}
}