  Successful visual navigation depends upon capturing images that contain
sufficient useful information. In this letter, we explore a data-driven
approach to account for environmental lighting changes, improving the quality
of images for use in visual odometry (VO) or visual simultaneous localization
and mapping (SLAM). We train a deep convolutional neural network model to
predictively adjust camera gain and exposure time parameters such that
consecutive images contain a maximal number of matchable features. The training
process is fully self-supervised: our training signal is derived from an
underlying VO or SLAM pipeline and, as a result, the model is optimized to
perform well with that specific pipeline. We demonstrate through extensive
real-world experiments that our network can anticipate and compensate for
dramatic lighting changes (e.g., transitions into and out of road tunnels),
maintaining a substantially higher number of inlier feature matches than
competing camera parameter control algorithms.
