  Visual Place Recognition (VPR) has been a subject of significant research
over the last 15 to 20 years. VPR is a fundamental task for autonomous
navigation as it enables self-localization within an environment. Although
robots are often equipped with resource-constrained hardware, the computational
requirements of and effects on VPR techniques have received little attention.
In this work, we present a hardware-focused benchmark evaluation of a number of
state-of-the-art VPR techniques on public datasets. We consider popular single
board computers, including ODroid, UP and Raspberry Pi 3, in addition to a
commodity desktop and laptop for reference. We present our analysis based on
several key metrics, including place-matching accuracy, image encoding time,
descriptor matching time and memory needs. Key questions addressed include: (1)
How does the performance accuracy of a VPR technique change with processor
architecture? (2) How does power consumption vary for different VPR techniques
and embedded platforms? (3) How much does descriptor size matter in comparison
to today's embedded platforms' storage? (4) How does the performance of a
high-end platform relate to an on-board low-end embedded platform for VPR? The
extensive analysis and results in this work serve not only as a benchmark for
the VPR community, but also provide useful insights for real-world adoption of
VPR applications.
