トポロジー特徴を用いた3D点群のための次最良視点学習
Learning the Next Best View for 3D Point Clouds via Topological Features
ノイズを含む3Dセンサの次最良視点を決めるため、トポロジーに基づく情報利得指標を用いた強化学習手法を提案し、穴や凹部などの詳細特徴を捉える視点配置を実現した。
著者: Christopher Collander, William J. Beksi, Manfred Huber
分類: cs.RO, cs.AI
原文アブストラクト
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.
