  We propose a methodology for lidar super-resolution with ground vehicles
driving on roadways, which relies completely on a driving simulator to enhance,
via deep learning, the apparent resolution of a physical lidar. To increase the
resolution of the point cloud captured by a sparse 3D lidar, we convert this
problem from 3D Euclidean space into an image super-resolution problem in 2D
image space, which is solved using a deep convolutional neural network. By
projecting a point cloud onto a range image, we are able to efficiently enhance
the resolution of such an image using a deep neural network. Typically, the
training of a deep neural network requires vast real-world data. Our approach
does not require any real-world data, as we train the network purely using
computer-generated data. Thus our method is applicable to the enhancement of
any type of 3D lidar theoretically. By novelly applying Monte-Carlo dropout in
the network and removing the predictions with high uncertainty, our method
produces high accuracy point clouds comparable with the observations of a real
high resolution lidar. We present experimental results applying our method to
several simulated and real-world datasets. We argue for the method's potential
benefits in real-world robotics applications such as occupancy mapping and
terrain modeling.
