  Visual Place Recognition (VPR) is the ability of a robotic platform to
correctly interpret visual stimuli from its on-board cameras in order to
determine whether it is currently located in a previously visited place,
despite different viewpoint, illumination and appearance changes. JPEG is a
widely used image compression standard that is capable of significantly
reducing the size of an image at the cost of image clarity. For applications
where several robotic platforms are simultaneously deployed, the visual data
gathered must be transmitted remotely between each robot. Hence, JPEG
compression can be employed to drastically reduce the amount of data
transmitted over a communication channel, as working with limited bandwidth for
VPR can be proven to be a challenging task. However, the effects of JPEG
compression on the performance of current VPR techniques have not been
previously studied. For this reason, this paper presents an in-depth study of
JPEG compression in VPR related scenarios. We use a selection of
well-established VPR techniques on well-established benchmark datasets with
various amounts of compression applied. We show that by introducing
compression, the VPR performance is drastically reduced, especially in the
higher spectrum of compression. Moreover, this paper demonstrates how
fine-tuning a CNN can be utilised as an optimisation method for JPEG compressed
data to perform more consistently with the image transformations detected in
extremely JPEG compressed images.
