Journal of the Japan Society for Precision Engineering
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
Paper
Best Next-Viewpoint Recommendation by Selecting Minimum Pose Ambiguity for Category-Level Object Pose Estimation
Nik Mohd Zarifie HASHIMYasutomo KAWANISHIDaisuke DEGUCHIIchiro IDEAyako AMMANorimasa KOBORIHiroshi MURASE
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2021 Volume 87 Issue 5 Pages 440-446

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Abstract

Object manipulation is one of the essential tasks for a home helper robot, especially in helping a disabled person to complete everyday tasks. For handling various objects in a category, accurate pose estimation of the target objects is required. Since the pose of an object is often ambiguous from an observation, it is important to select a good nextviewpoint to make a better pose estimation. This paper introduces a metric of the object pose ambiguity based on the entropy of the pose estimation result. By using the metric, a best next-viewpoint recommendation method is proposed for accurate category-level object pose estimation. Evaluation is performed with synthetic object images of objects in five categories. It shows the proposed methods is applicable to various kind of object categories.

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© 2021 The Japan Society for Precision Engineering
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