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In this work we address the problem of autonomous 3D exploration of anunknown indoor environment using a depth camera. We cast the problem as theestimation of the Next Best View (NBV) that maximises the coverage of theunknown area. We do this by re-formulating NBV estimation as a classificationproblem and we propose a novel learning-based metric that encodes both, thecurrent 3D observation (a depth frame) and the history of the ongoingreconstruction. One of the major contributions of this work is aboutintroducing a new representation for the 3D reconstruction history as anauxiliary utility map which is efficiently coupled with the current depthobservation. With both pieces of information, we train a light-weight CNN,named ExHistCNN, that estimates the NBV as a set of directions towards whichthe depth sensor finds most unexplored areas. We perform extensive evaluationon both synthetic and real room scans demonstrating that the proposed ExHistCNNis able to approach the exploration performance of an oracle using the completeknowledge of the 3D environment.

Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration