  The use of local detectors and descriptors in typical computer vision
pipelines work well until variations in viewpoint and appearance change become
extreme. Past research in this area has typically focused on one of two
approaches to this challenge: the use of projections into spaces more suitable
for feature matching under extreme viewpoint changes, and attempting to learn
features that are inherently more robust to viewpoint change. In this paper, we
present a novel framework that combines learning of invariant descriptors
through data augmentation and orthographic viewpoint projection. We propose
rotation-robust local descriptors, learnt through training data augmentation
based on rotation homographies, and a correspondence ensemble technique that
combines vanilla feature correspondences with those obtained through
rotation-robust features. Using a range of benchmark datasets as well as
contributing a new bespoke dataset for this research domain, we evaluate the
effectiveness of the proposed approach on key tasks including pose estimation
and visual place recognition. Our system outperforms a range of baseline and
state-of-the-art techniques, including enabling higher levels of place
recognition precision across opposing place viewpoints and achieves
practically-useful performance levels even under extreme viewpoint changes.
