  We study how autonomous robots can learn by themselves to improve their depth
estimation capability. In particular, we investigate a self-supervised learning
setup in which stereo vision depth estimates serve as targets for a
convolutional neural network (CNN) that transforms a single still image to a
dense depth map. After training, the stereo and mono estimates are fused with a
novel fusion method that preserves high confidence stereo estimates, while
leveraging the CNN estimates in the low-confidence regions. The main
contribution of the article is that it is shown that the fused estimates lead
to a higher performance than the stereo vision estimates alone. Experiments are
performed on the KITTI dataset, and on board of a Parrot SLAMDunk, showing that
even rather limited CNNs can help provide stereo vision equipped robots with
more reliable depth maps for autonomous navigation.
