  We present semi-supervised deep learning approaches for traversability
estimation from fisheye images. Our method, GONet, and the proposed extensions
leverage Generative Adversarial Networks (GANs) to effectively predict whether
the area seen in the input image(s) is safe for a robot to traverse. These
methods are trained with many positive images of traversable places, but just a
small set of negative images depicting blocked and unsafe areas. This makes the
proposed methods practical. Positive examples can be collected easily by simply
operating a robot through traversable spaces, while obtaining negative examples
is time consuming, costly, and potentially dangerous. Through extensive
experiments and several demonstrations, we show that the proposed
traversability estimation approaches are robust and can generalize to unseen
scenarios. Further, we demonstrate that our methods are memory efficient and
fast, allowing for real-time operation on a mobile robot with single or stereo
fisheye cameras. As part of our contributions, we open-source two new datasets
for traversability estimation. These datasets are composed of approximately 24h
of videos from more than 25 indoor environments. Our methods outperform
baseline approaches for traversability estimation on these new datasets.
