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.
@article{arxiv.2103.02789,
title = {Learning the Next Best View for 3D Point Clouds via Topological Features},
author = {Christopher Collander and William J. Beksi and Manfred Huber},
journal= {arXiv preprint arXiv:2103.02789},
year = {2021}
}
Comments
To be published in the 2021 IEEE International Conference on Robotics and Automation (ICRA)