  A reliable sense-and-avoid system is critical to enabling safe autonomous
operation of unmanned aircraft. Existing sense-and-avoid methods often require
specialized sensors that are too large or power intensive for use on small
unmanned vehicles. This paper presents a method to estimate object distances
based on visual image sequences, allowing for the use of low-cost, on-board
monocular cameras as simple collision avoidance sensors. We present a deep
recurrent convolutional neural network and training method to generate depth
maps from video sequences. Our network is trained using simulated camera and
depth data generated with Microsoft's AirSim simulator. Empirically, we show
that our model achieves superior performance compared to models generated using
prior methods.We further demonstrate that the method can be used for
sense-and-avoid of obstacles in simulation.
