  One recent promising approach to the Visual Place Recognition (VPR) problem
has been to fuse the place recognition estimates of multiple complementary VPR
techniques using methods such as SRAL and multi-process fusion. These
approaches come with a substantial practical limitation: they require all
potential VPR methods to be brute-force run before they are selectively fused.
The obvious solution to this limitation is to predict the viable subset of
methods ahead of time, but this is challenging because it requires a predictive
signal within the imagery itself that is indicative of high performance
methods. Here we propose an alternative approach that instead starts with a
known single base VPR technique, and learns to predict the most complementary
additional VPR technique to fuse with it, that results in the largest
improvement in performance. The key innovation here is to use a dimensionally
reduced difference vector between the query image and the top-retrieved
reference image using this baseline technique as the predictive signal of the
most complementary additional technique, both during training and inference. We
demonstrate that our approach can train a single network to select performant,
complementary technique pairs across datasets which span multiple modes of
transportation (train, car, walking) as well as to generalise to unseen
datasets, outperforming multiple baseline strategies for manually selecting the
best technique pairs based on the same training data.

Computer Vision and Pattern Recognition;Robotics