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Udit Singh Parihar – CSE

Udit Singh Parihar received his MS in Computer Science and Engineering (CSE). His research work was supervised by Prof. Madhava Krishna. Here’s a summary of  his research work on RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching:

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. We reduce pose estimation error by 86.72 % relative to state of the art. 

In particular, our integration of Visual Place Recognition (VPR) with SLAM by leveraging the robustness of deep-learnt features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence and pose graph sub-modules of the SLAM pipeline. We demonstrate a localization system capable of state-of-the-art performance despite perceptual aliasing and extreme 180-degree-rotated viewpoint change in a range of real-world and simulated experiments. Our system is able to achieve early loop closures that prevent significant drifts in SLAM trajectories.