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.
翻译:在本文中,我们采用了一种强化学习方法,利用基于地形的新信息获取量度来引导对噪音的3D传感器的下一个最佳视图。该量度将观测表面的断裂部分结合起来,侧重于洞和凝结部分等高细节特征。实验结果显示,我们的方法可以帮助建立机器人传感器的位置,以优化其流点云数据提供的信息。此外,3D物体的标签数据集、定制机器人操纵器的CAD设计以及点云转换、结合和登记软件已经公开发布给研究界。