Learning the Next Best View for 3D Point Clouds via Topological Features
In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave se...
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Published in | 2021 IEEE International Conference on Robotics and Automation (ICRA) pp. 12207 - 12213 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
30.05.2021
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Subjects | |
Online Access | Get full text |
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Summary: | In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave sections. Experimental results show that our approach can aid in establishing the placement of a robotic sensor to optimize the information provided by its streaming point cloud data. Furthermore, a labeled dataset of 3D objects, a CAD design for a custom robotic manipulator, and software for the transformation, union, and registration of point clouds has been publicly released to the research community. |
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ISSN: | 2577-087X |
DOI: | 10.1109/ICRA48506.2021.9561389 |