Recent visual pose estimation and tracking solutions provide notable results on popular datasets such as T-LESS and YCB. However, in the real world, we can find ambiguous objects that do not allow exact classification and detection from a single view. In this work, we propose a framework that, given a single view of an object, provides the coordinates of a next viewpoint to discriminate the object against similar ones, if any, and eliminates ambiguities. We also describe a complete pipeline from a real object's scans to the viewpoint selection and classification. We validate our approach with a Franka Emika Panda robot and common household objects featured with ambiguities. We released the source code to reproduce our experiments.
@article{arxiv.2108.00737,
title = {Active Perception for Ambiguous Objects Classification},
author = {Evgenii Safronov and Nicola Piga and Michele Colledanchise and Lorenzo Natale},
journal= {arXiv preprint arXiv:2108.00737},
year = {2022}
}
Comments
Accepted version at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)