  Mobile robots and autonomous vehicles are often required to function in
environments where critical position estimates from sensors such as GPS become
uncertain or unreliable. Single image visual place recognition (VPR) provides
an alternative for localization but often requires techniques such as sequence
matching to improve robustness, which incurs additional computation and latency
costs. Even then, the sequence length required to localize at an acceptable
performance level varies widely; and simply setting overly long fixed sequence
lengths creates unnecessary latency, computational overhead, and can even
degrade performance. In these scenarios it is often more desirable to meet or
exceed a set target performance at minimal expense. In this paper we present an
approach which uses a calibration set of data to fit a model that modulates
sequence length for VPR as needed to exceed a target localization performance.
We make use of a coarse position prior, which could be provided by any other
localization system, and capture the variation in appearance across this
region. We use the correlation between appearance variation and sequence length
to curate VPR features and fit a multilayer perceptron (MLP) for selecting the
optimal length. We demonstrate that this method is effective at modulating
sequence length to maximize the number of sections in a dataset which meet or
exceed a target performance whilst minimizing the median length used. We show
applicability across several datasets and reveal key phenomena like
generalization capabilities, the benefits of curating features and the utility
of non-state-of-the-art feature extractors with nuanced properties.
