  We consider the problem of time-limited robotic exploration in previously
unseen environments where exploration is limited by a predefined amount of
time. We propose a novel exploration approach using learning-augmented
model-based planning. We generate a set of subgoals associated with frontiers
on the current map and derive a Bellman Equation for exploration with these
subgoals. Visual sensing and advances in semantic mapping of indoor scenes are
exploited for training a deep convolutional neural network to estimate
properties associated with each frontier: the expected unobserved area beyond
the frontier and the expected timesteps (discretized actions) required to
explore it. The proposed model-based planner is guaranteed to explore the whole
scene if time permits. We thoroughly evaluate our approach on a large-scale
pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We
compare our approach with classical and more recent RL-based exploration
methods. Our approach surpasses the greedy strategies by 2.1% and the RL-based
exploration methods by 8.4% in terms of coverage.
