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.
A Sparse Octree-Based CNN for Probabilistic Occupancy Prediction Applied to Next Best View Planning / Monica, R.; Aleotti, J.. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 9:11(2024), pp. 9359-9366. [10.1109/LRA.2024.3460432]
A Sparse Octree-Based CNN for Probabilistic Occupancy Prediction Applied to Next Best View Planning
Monica R.
;Aleotti J.
2024-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.