  In autonomous navigation of mobile robots, sensors suffer from massive
occlusion in cluttered environments, leaving significant amount of space
unknown during planning. In practice, treating the unknown space in optimistic
or pessimistic ways both set limitations on planning performance, thus
aggressiveness and safety cannot be satisfied at the same time. However, humans
can infer the exact shape of the obstacles from only partial observation and
generate non-conservative trajectories that avoid possible collisions in
occluded space. Mimicking human behavior, in this paper, we propose a method
based on deep neural network to predict occupancy distribution of unknown space
reliably. Specifically, the proposed method utilizes contextual information of
environments and learns from prior knowledge to predict obstacle distributions
in occluded space. We use unlabeled and no-ground-truth data to train our
network and successfully apply it to real-time navigation in unseen
environments without any refinement. Results show that our method leverages the
performance of a kinodynamic planner by improving security with no reduction of
speed in clustered environments.
