Estimating map completeness in robot exploration

We present a novel method that, given a grid map of a partially explored indoor environment, estimates the amount of the explored area in the map and whether it is worth continuing to explore the uncovered part of the environment. Our method is based on the idea that modern deep learning models can successfully solve this task by leveraging visual clues in the map. Thus, we train a deep convolutional neural network on images depicting grid maps from partially explored environments, with annotations derived from the knowledge of the entire map, which is not available when the network is used for inference. We show that our network can be used to define a stopping criterion to successfully terminate the exploration process when this is expected to no longer add relevant details about the environment to the map, saving more than 35% of the total exploration time compared to covering the whole environment area.