Autonomous 3-D Reconstruction, Mapping, and Exploration of Indoor Environments With a Robotic Arm

We propose a novel information gain metric that combines hand-crafted and data-driven metrics to address the next best view problem for autonomous 3-D mapping of unknown indoor environments. For the hand-crafted metric, we propose an entropy-based information gain that accounts for the previous view points to avoid the camera to revisit the same location and to promote the motion toward unexplored or occluded areas. However, for the learnt metric, we adopt a convolutional neural network (CNN) architecture and formulate the problem as a classification problem. The CNN takes the current depth image as input and outputs the motion direction that suggests the largest unexplored surface. We train and test the CNN using a new synthetic dataset based on the SUNCG dataset. The learnt motion direction is then combined with the proposed hand-crafted metric to help handle situations where using only the hand-crafted metric tends to face ambiguities. We finally evaluate the autonomous paths over several real and synthetic indoor scenes including complex industrial and domestic settings and prove that our combined metric is able to further improve the exploration coverage compared to using only the proposed hand-crafted metric.