Best next-viewpoint recommendation by selecting minimum pose ambiguity for category-level object pose estimation

Hashim, Nik Mohd Zarifie and Kawanishi, Yasutomo and Deguchi, Daisuke and Ide, Ichiro and Murase, Hiroshi and Amma, Ayako and Kobori, Norimasa (2021) Best next-viewpoint recommendation by selecting minimum pose ambiguity for category-level object pose estimation. Journal of the Japan Society for Precision Engineering, 87 (5). pp. 440-446. ISSN 0912-0289

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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 next-viewpoint 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.

Item Type:	Article
Uncontrolled Keywords:	Best next viewpoint, Category-level object pose estimation, Entropy, Human helper robot, Pose ambiguity
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	30 May 2022 11:58
Last Modified:	06 Jun 2023 16:35
URI:	http://eprints.utem.edu.my/id/eprint/25968
Statistic Details:	View Download Statistic

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