Title: Towards CNN map representation and compression for camera relocalisation,
Abstract: 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 the response to different
data inputs. We use a CNN map representation and introduce the notion of map
compression under this paradigm by using smaller CNN architectures without
sacrificing relocalisation performance. We evaluate this approach in a series
of publicly available datasets over a number of CNN architectures with
different sizes, both in complexity and number of layers. This formulation
allows us to improve relocalisation accuracy by increasing the number of
training trajectories while maintaining a constant-size CNN.