  Vision-based depth reconstruction is a challenging problem extensively
studied in computer vision but still lacking universal solution. Reconstructing
depth from single image is particularly valuable to mobile robotics as it can
be embedded to the modern vision-based simultaneous localization and mapping
(vSLAM) methods providing them with the metric information needed to construct
accurate maps in real scale. Typically, depth reconstruction is done nowadays
via fully-convolutional neural networks (FCNNs). In this work we experiment
with several FCNN architectures and introduce a few enhancements aimed at
increasing both the effectiveness and the efficiency of the inference. We
experimentally determine the solution that provides the best
performance/accuracy tradeoff and is able to run on NVidia Jetson with the
framerates exceeding 16FPS for 320 x 240 input. We also evaluate the suggested
models by conducting monocular vSLAM of unknown indoor environment on NVidia
Jetson TX2 in real-time. Open-source implementation of the models and the
inference node for Robot Operating System (ROS) are available at
https://github.com/CnnDepth/tx2_fcnn_node.
