Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration
This work provides an incremental improvement for autonomous 3D exploration systems, particularly for robots or drones operating in unknown indoor environments.
This paper addresses autonomous 3D exploration in unknown indoor environments by reformulating Next Best View (NBV) estimation as a classification problem. They propose ExHistCNN, a lightweight CNN that combines current depth observations with a novel representation of 3D reconstruction history to estimate NBV directions. The method approaches the exploration performance of an oracle with complete environment knowledge.
In this work we address the problem of autonomous 3D exploration of an unknown indoor environment using a depth camera. We cast the problem as the estimation of the Next Best View (NBV) that maximises the coverage of the unknown area. We do this by re-formulating NBV estimation as a classification problem and we propose a novel learning-based metric that encodes both, the current 3D observation (a depth frame) and the history of the ongoing reconstruction. One of the major contributions of this work is about introducing a new representation for the 3D reconstruction history as an auxiliary utility map which is efficiently coupled with the current depth observation. With both pieces of information, we train a light-weight CNN, named ExHistCNN, that estimates the NBV as a set of directions towards which the depth sensor finds most unexplored areas. We perform extensive evaluation on both synthetic and real room scans demonstrating that the proposed ExHistCNN is able to approach the exploration performance of an oracle using the complete knowledge of the 3D environment.