Udit Singh Parihar*1 Aniket Gujarathi*1 Kinal Mehta*1 Satyajit Tourani*1 Sourav Garg2 Michael Milford2 K. Madhava Krishna1
1 Robotics Research Center, IIIT Hyderabad 2 QUT Centre for Robotics, Queensland University of Technology (QUT), Australia
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.