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

Abstract

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.

References in corpus (5)

• The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
• EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
• Event-based visual place recognition with ensembles of temporal windows
• Place recognition: An Overview of Vision Perspective
• Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM