Active Perception for Ambiguous Objects Classification

• URL: http://arxiv.org/abs/2108.00737v1
• Date: Mon, 2 Aug 2021 09:12:34 GMT
• Title: Active Perception for Ambiguous Objects Classification
• Authors: Evgenii Safronov, Nicola Piga, Michele Colledanchise, and Lorenzo Natale
• Abstract summary: In the real world, we can find ambiguous objects that do not allow exact classification and detection from a single view. 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 validate our approach with a Franka Emika Panda robot and common household objects featured with ambiguities.
• Score: 7.837959746116199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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