  Images incorporate a wealth of information from a robot's surroundings. With
the widespread availability of compact cameras, visual information has become
increasingly popular for addressing the localisation problem, which is then
termed as Visual Place Recognition (VPR). While many applications use
high-resolution cameras and high-end systems to achieve optimal place-matching
performance, low-end commercial systems face limitations due to resource
constraints and relatively low-resolution and low-quality cameras. In this
paper, we analyse the effects of image resolution on the accuracy and
robustness of well-established handcrafted VPR pipelines. Handcrafted designs
have low computational demands and can adapt to flexible image resolutions,
making them a suitable approach to scale to any image source and to operate
under resource limitations. This paper aims to help academic researchers and
companies in the hardware and software industry co-design VPR solutions and
expand the use of VPR algorithms in commercial products.
