Cross-modal Place Recognition in Image Databases using Event-based
  Sensors
Xiang Ji Jiaxin Wei Yifu Wang Huiliang Shang Laurent Kneip
Visual place recognition is an important problem towards global localization
in many robotics tasks. One of the biggest challenges is that it may suffer
from illumination or appearance changes in surrounding environments. Event
cameras are interesting alternatives to frame-based sensors as their high
dynamic range enables robust perception in difficult illumination conditions.
However, current event-based place recognition methods only rely on event
information, which restricts downstream applications of VPR. In this paper, we
present the first cross-modal visual place recognition framework that is
capable of retrieving regular images from a database given an event query. Our
method demonstrates promising results with respect to the state-of-the-art
frame-based and event-based methods on the Brisbane-Event-VPR dataset under
different scenarios. We also verify the effectiveness of the combination of
retrieval and classification, which can boost performance by a large margin.