Best next-viewpoint recommendation by selecting minimum pose ambiguity for category-level object pose estimation
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 a...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Japan Society for Precision Engineering
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25968/2/87_440.PDF http://eprints.utem.edu.my/id/eprint/25968/ https://www.jstage.jst.go.jp/article/jjspe/87/5/87_440/_pdf/-char/en |
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| Summary: | 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. |
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