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arXiv · 2108.00737

Active Perception for Ambiguous Objects Classification

Evgenii Safronov·Nicola Piga·Michele Colledanchise·Lorenzo Natale
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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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Evgenii Safronov, Nicola Piga, Michele Colledanchise, Lorenzo Natale. 2021-08-02. Active Perception for Ambiguous Objects Classification. https://doi.org/10.1109/iros51168.2021.9636414

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