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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Bibliographic Details
Published in2021 IEEE International Conference on Robotics and Automation (ICRA) pp. 12207 - 12213
Main Authors Collander, Christopher, Beksi, William J., Huber, Manfred
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.05.2021
Subjects
Conferences
Manipulators
Reinforcement learning
Robot kinematics
Robot sensing systems
Solid modeling
Three-dimensional displays
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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.
ISSN:2577-087X
DOI:10.1109/ICRA48506.2021.9561389