A Sparse Octree-Based CNN for Probabilistic Occupancy Prediction Applied to Next Best View Planning

Abstract

This work proposes OcLe-CNN, a sparse octree-based Convolutional Neural Network (CNN) for 3D occupancy prediction. Occupancy prediction involves the inference of the occupancy probability of unobserved space. OcLe-CNN processes an octree-like data structure resulting in a reduced memory usage, as resources are allocated prevalently in the most detail-rich regions of the environment. Also, a novel loss function is introduced which results in smaller octrees compared to the state-of-the-art Structure and Task loss. The proposed CNN was integrated with a probabilistic robot Next Best View (NBV) planner, where an octree-like data structure speeds up the ray casting stage. The integration resulted in a lower total computation time. The method was implemented for both quadtrees and octrees, and it was validated on 2D and 3D datasets as well as on a real robot manipulator setup.