  Event cameras continue to attract interest due to desirable characteristics
such as high dynamic range, low latency, virtually no motion blur, and high
energy efficiency. One of the potential applications that would benefit from
these characteristics lies in visual place recognition for robot localization,
i.e. matching a query observation to the corresponding reference place in the
database. In this letter, we explore the distinctiveness of event streams from
a small subset of pixels (in the tens or hundreds). We demonstrate that the
absolute difference in the number of events at those pixel locations
accumulated into event frames can be sufficient for the place recognition task,
when pixels that display large variations in the reference set are used. Using
such sparse (over image coordinates) but varying (variance over the number of
events per pixel location) pixels enables frequent and computationally cheap
updates of the location estimates. Furthermore, when event frames contain a
constant number of events, our method takes full advantage of the event-driven
nature of the sensory stream and displays promising robustness to changes in
velocity. We evaluate our proposed approach on the Brisbane-Event-VPR dataset
in an outdoor driving scenario, as well as the newly contributed indoor
QCR-Event-VPR dataset that was captured with a DAVIS346 camera mounted on a
mobile robotic platform. Our results show that our approach achieves
competitive performance when compared to several baseline methods on those
datasets, and is particularly well suited for compute- and energy-constrained
platforms such as interplanetary rovers.
