  Visual Place Recognition (VPR) is a critical task for performing global
re-localization in visual perception systems. It requires the ability to
accurately recognize a previously visited location under variations such as
illumination, occlusion, appearance and viewpoint. In the case of robotic
systems and augmented reality, the target devices for deployment are battery
powered edge devices. Therefore whilst the accuracy of VPR methods is important
so too is memory consumption and latency. Recently new works have focused on
the recall@1 metric as a performance measure with limited focus on resource
utilization. This has resulted in methods that use deep learning models too
large to deploy on low powered edge devices. We hypothesize that these large
models are highly over-parameterized and can be optimized to satisfy the
constraints of a low powered embedded system whilst maintaining high recall
performance. Our work studies the impact of compact convolutional network
architecture design in combination with full-precision and mixed-precision
post-training quantization on VPR performance. Importantly we not only measure
performance via the recall@1 score but also measure memory consumption and
latency. We characterize the design implications on memory, latency and recall
scores and provide a number of design recommendations for VPR systems under
these resource limitations.
