Spike-EVPR: Deep Spiking Residual Network with Cross-Representation
  Aggregation for Event-Based Visual Place Recognition
Chenming Hu Zheng Fang Kuanxu Hou Delei Kong Junjie Jiang Hao Zhuang Mingyuan Sun Xinjie Huang
Event cameras have been successfully applied to visual place recognition
(VPR) tasks by using deep artificial neural networks (ANNs) in recent years.
However, previously proposed deep ANN architectures are often unable to harness
the abundant temporal information presented in event streams. In contrast, deep
spiking networks exhibit more intricate spatiotemporal dynamics and are
inherently well-suited to process sparse asynchronous event streams.
Unfortunately, directly inputting temporal-dense event volumes into the spiking
network introduces excessive time steps, resulting in prohibitively high
training costs for large-scale VPR tasks. To address the aforementioned issues,
we propose a novel deep spiking network architecture called Spike-EVPR for
event-based VPR tasks. First, we introduce two novel event representations
tailored for SNN to fully exploit the spatio-temporal information from the
event streams, and reduce the video memory occupation during training as much
as possible. Then, to exploit the full potential of these two representations,
we construct a Bifurcated Spike Residual Encoder (BSR-Encoder) with powerful
representational capabilities to better extract the high-level features from
the two event representations. Next, we introduce a Shared & Specific
Descriptor Extractor (SSD-Extractor). This module is designed to extract
features shared between the two representations and features specific to each.
Finally, we propose a Cross-Descriptor Aggregation Module (CDA-Module) that
fuses the above three features to generate a refined, robust global descriptor
of the scene. Our experimental results indicate the superior performance of our
Spike-EVPR compared to several existing EVPR pipelines on Brisbane-Event-VPR
and DDD20 datasets, with the average Recall@1 increased by 7.61% on Brisbane
and 13.20% on DDD20.