  This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate response to different
data inputs -- namely, depth, grayscale, RGB, spatial position and combinations
of these. We use a CNN map representation and introduce the notion of CNN map
compression by using a smaller CNN architecture. We evaluate our proposal in a
series of publicly available datasets. This formulation allows us to improve
relocalisation accuracy by increasing the number of training trajectories while
maintaining a constant-size CNN.
