  We consider scenarios where a ground vehicle plans its path using data
gathered by an aerial vehicle. In the aerial images, navigable areas of the
scene may be occluded due to obstacles. Naively planning paths using aerial
images may result in longer paths as a conservative planner may try to avoid
regions that are occluded. We propose a modular, deep learning-based framework
that allows the robot to predict the existence of navigable areas in the
occluded regions. Specifically, we use image inpainting methods to fill in
parts of the areas that are potentially occluded, which can then be
semantically segmented to determine navigability. We use supervised neural
networks for both modules. However, these predictions may be incorrect.
Therefore, we extract uncertainty in these predictions and use a risk-aware
approach that takes these uncertainties into account for path planning. We
compare modules in our approach with non-learning-based approaches to show the
efficacy of the proposed framework through photo-realistic simulations. The
modular pipeline allows further improvement in path planning and deployment in
different settings.
