  While an exciting diversity of new imaging devices is emerging that could
dramatically improve robotic perception, the challenges of calibrating and
interpreting these cameras have limited their uptake in the robotics community.
In this work we generalise techniques from unsupervised learning to allow a
robot to autonomously interpret new kinds of cameras. We consider emerging
sparse light field (LF) cameras, which capture a subset of the 4D LF function
describing the set of light rays passing through a plane. We introduce a
generalised encoding of sparse LFs that allows unsupervised learning of
odometry and depth. We demonstrate the proposed approach outperforming
monocular and conventional techniques for dealing with 4D imagery, yielding
more accurate odometry and depth maps and delivering these with metric scale.
We anticipate our technique to generalise to a broad class of LF and sparse LF
cameras, and to enable unsupervised recalibration for coping with shifts in
camera behaviour over the lifetime of a robot. This work represents a first
step toward streamlining the integration of new kinds of imaging devices in
robotics applications.
