  Recognising previously visited locations is an important, but unsolved, task
in autonomous navigation. Current visual place recognition (VPR) benchmarks
typically challenge models to recover the position of a query image (or images)
from sequential datasets that include both spatial and temporal components.
Recently, Echo State Network (ESN) varieties have proven particularly powerful
at solving machine learning tasks that require spatio-temporal modelling. These
networks are simple, yet powerful neural architectures that--exhibiting memory
over multiple time-scales and non-linear high-dimensional representations--can
discover temporal relations in the data while still maintaining linearity in
the learning time. In this paper, we present a series of ESNs and analyse their
applicability to the VPR problem. We report that the addition of ESNs to
pre-processed convolutional neural networks led to a dramatic boost in
performance in comparison to non-recurrent networks in five out of six standard
benchmarks (GardensPoint, SPEDTest, ESSEX3IN1, Oxford RobotCar, and Nordland),
demonstrating that ESNs are able to capture the temporal structure inherent in
VPR problems. Moreover, we show that models that include ESNs can outperform
class-leading VPR models which also exploit the sequential dynamics of the
data. Finally, our results demonstrate that ESNs improve generalisation
abilities, robustness, and accuracy further supporting their suitability to VPR
applications.
