  This paper contributes a novel learning-based method for aggressive
task-driven compression of depth images and their encoding as images tailored
to collision prediction for robotic systems. A novel 3D image processing
methodology is proposed that accounts for the robot's size in order to
appropriately "inflate" the obstacles represented in the depth image and thus
obtain the distance that can be traversed by the robot in a collision-free
manner along any given ray within the camera frustum. Such depth-and-collision
image pairs are used to train a neural network that follows the architecture of
Variational Autoencoders to compress-and-transform the information in the
original depth image to derive a latent representation that encodes the
collision information for the given depth image. We compare our proposed
task-driven encoding method with classical task-agnostic methods and
demonstrate superior performance for the task of collision image prediction
from extremely low-dimensional latent spaces. A set of comparative studies show
that the proposed approach is capable of encoding depth image-and-collision
image tuples from complex scenes with thin obstacles at long distances better
than the classical methods at compression ratios as high as 4050:1.
