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Traditional visual place recognition (VPR) methods generally use frame-basedcameras, which is easy to fail due to dramatic illumination changes or fastmotions. In this paper, we propose an end-to-end visual place recognitionnetwork for event cameras, which can achieve good place recognition performancein challenging environments. The key idea of the proposed algorithm is firstlyto characterize the event streams with the EST voxel grid, then extractfeatures using a convolution network, and finally aggregate features using animproved VLAD network to realize end-to-end visual place recognition usingevent streams. To verify the effectiveness of the proposed algorithm, wecompare the proposed method with classical VPR methods on the event-baseddriving datasets (MVSEC, DDD17) and the synthetic datasets (Oxford RobotCar).Experimental results show that the proposed method can achieve much betterperformance in challenging scenarios. To our knowledge, this is the firstend-to-end event-based VPR method. The accompanying source code is available athttps://github.com/kongdelei/Event-VPR.

Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition