  Event cameras are increasingly popular in robotics due to their beneficial
features, such as low latency, energy efficiency, and high dynamic range.
Nevertheless, their downstream task performance is greatly influenced by the
optimization of bias parameters. These parameters, for instance, regulate the
necessary change in light intensity to trigger an event, which in turn depends
on factors such as the environment lighting and camera motion. This paper
introduces feedback control algorithms that automatically tune the bias
parameters through two interacting methods: 1) An immediate, on-the-fly fast
adaptation of the refractory period, which sets the minimum interval between
consecutive events, and 2) if the event rate exceeds the specified bounds even
after changing the refractory period repeatedly, the controller adapts the
pixel bandwidth and event thresholds, which stabilizes after a short period of
noise events across all pixels (slow adaptation). Our evaluation focuses on the
visual place recognition task, where incoming query images are compared to a
given reference database. We conducted comprehensive evaluations of our
algorithms' adaptive feedback control in real-time. To do so, we collected the
QCR-Fast-and-Slow dataset that contains DAVIS346 event camera streams from 366
repeated traversals of a Scout Mini robot navigating through a 100 meter long
indoor lab setting (totaling over 35km distance traveled) in varying brightness
conditions with ground truth location information. Our proposed feedback
controllers result in superior performance when compared to the standard bias
settings and prior feedback control methods. Our findings also detail the
impact of bias adjustments on task performance and feature ablation studies on
the fast and slow adaptation mechanisms.
