Amaldev Haridevan

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About

Amaldev Haridevan is a researcher specializing in computer vision and robotics, with a particular focus on fiducial marker detection, motion deblurring, and point cloud registration. His work addresses critical real-world challenges in robotic perception, where environmental conditions such as motion blur can significantly hinder system reliability and accuracy. One of his notable contributions is the development of Ghost-DeblurGAN, a lightweight generative adversarial network designed for real-time motion deblurring, specifically targeting fiducial marker detection in dynamic robotic environments. This work, published in 2022 and garnering 16 citations, demonstrates both technical innovation and practical applicability in robotics pipelines. More recently, his research on L-PR introduces a novel LiDAR fiducial marker-based approach for multiview point cloud registration under challenging low-overlap conditions, pushing the boundaries of 3D scene understanding in out-of-distribution scenarios. Haridevan's research sits at the intersection of deep learning and robotics perception, contributing solutions that are computationally efficient and robust under real-world constraints. His growing citation record reflects an emerging influence in the robotics and computer vision communities, making his work particularly valuable for students and engineers developing reliable autonomous systems.