  Traditional visual place recognition (VPR) methods generally use frame-based
cameras, which is easy to fail due to dramatic illumination changes or fast
motions. In this paper, we propose an end-to-end visual place recognition
network for event cameras, which can achieve good place recognition performance
in challenging environments. The key idea of the proposed algorithm is firstly
to characterize the event streams with the EST voxel grid, then extract
features using a convolution network, and finally aggregate features using an
improved VLAD network to realize end-to-end visual place recognition using
event streams. To verify the effectiveness of the proposed algorithm, we
compare the proposed method with classical VPR methods on the event-based
driving datasets (MVSEC, DDD17) and the synthetic datasets (Oxford RobotCar).
Experimental results show that the proposed method can achieve much better
performance in challenging scenarios. To our knowledge, this is the first
end-to-end event-based VPR method. The accompanying source code is available at
https://github.com/kongdelei/Event-VPR.
