  Depth sensing is a critical function for robotic tasks such as localization,
mapping and obstacle detection. There has been a significant and growing
interest in depth estimation from a single RGB image, due to the relatively low
cost and size of monocular cameras. However, state-of-the-art single-view depth
estimation algorithms are based on fairly complex deep neural networks that are
too slow for real-time inference on an embedded platform, for instance, mounted
on a micro aerial vehicle. In this paper, we address the problem of fast depth
estimation on embedded systems. We propose an efficient and lightweight
encoder-decoder network architecture and apply network pruning to further
reduce computational complexity and latency. In particular, we focus on the
design of a low-latency decoder. Our methodology demonstrates that it is
possible to achieve similar accuracy as prior work on depth estimation, but at
inference speeds that are an order of magnitude faster. Our proposed network,
FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using
only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves
close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of
the authors' knowledge, this paper demonstrates real-time monocular depth
estimation using a deep neural network with the lowest latency and highest
throughput on an embedded platform that can be carried by a micro aerial
vehicle.
