Papers
1
Total Citations
3
H-Index
1
About
Chinmay Samant is a researcher whose work centers on advancing robust estimation techniques in robotics and computer vision, with a particular focus on sensor calibration and geometric perception. His most notable contribution is the development of a novel approach to Hand-Eye calibration, a fundamental problem in robotics that involves estimating the rigid transformation between two attached reference frames—such as a camera and a robotic arm—from noisy motion measurements. In his 2019 paper, Samant introduced a method that leverages iteratively re-weighted rank-constrained semi-definite programming to achieve robust calibration even when motion data is corrupted by synchronization errors or hardware imperfections. This work provides a principled, optimization-based framework that outperforms traditional linear methods in accuracy and resilience to outliers. While his citation count is currently modest, the paper represents a meaningful step forward in handling real-world sensor noise, offering a valuable tool for practitioners in autonomous systems and manipulation. Samant’s research sits at the intersection of optimization theory and applied robotics, demonstrating a commitment to solving practical challenges with mathematical rigor.
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Top Papers
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