  Visual place recognition (VPR) is a robot's ability to determine whether a
place was visited before using visual data. While conventional hand-crafted
methods for VPR fail under extreme environmental appearance changes, those
based on convolutional neural networks (CNNs) achieve state-of-the-art
performance but result in heavy runtime processes and model sizes that demand a
large amount of memory. Hence, CNN-based approaches are unsuitable for
resource-constrained platforms, such as small robots and drones. In this paper,
we take a multi-step approach of decreasing the precision of model parameters,
combining it with network depth reduction and fewer neurons in the classifier
stage to propose a new class of highly compact models that drastically reduces
the memory requirements and computational effort while maintaining
state-of-the-art VPR performance. To the best of our knowledge, this is the
first attempt to propose binary neural networks for solving the visual place
recognition problem effectively under changing conditions and with
significantly reduced resource requirements. Our best-performing binary neural
network, dubbed FloppyNet, achieves comparable VPR performance when considered
against its full-precision and deeper counterparts while consuming 99% less
memory and increasing the inference speed seven times.
