Unsupervised Quality Prediction for Improved Single-Frame and Weighted
  Sequential Visual Place Recognition
Helen Carson Jason J. Ford Michael Milford
While substantial progress has been made in the absolute performance of
localization and Visual Place Recognition (VPR) techniques, it is becoming
increasingly clear from translating these systems into applications that other
capabilities like integrity and predictability are just as important,
especially for safety- or operationally-critical autonomous systems. In this
research we present a new, training-free approach to predicting the likely
quality of localization estimates, and a novel method for using these
predictions to bias a sequence-matching process to produce additional
performance gains beyond that of a naive sequence matching approach. Our
combined system is lightweight, runs in real-time and is agnostic to the
underlying VPR technique. On extensive experiments across four datasets and
three VPR techniques, we demonstrate our system improves precision performance,
especially at the high-precision/low-recall operating point. We also present
ablation and analysis identifying the performance contributions of the
prediction and weighted sequence matching components in isolation, and the
relationship between the quality of the prediction system and the benefits of
the weighted sequential matcher.