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About

Mihai Jalobeanu is a roboticist focused on making indoor mobile robot navigation more practical, reliable, and accessible. His research centers on benchmarking, localization, and cost-effective sensing solutions for autonomous systems. Jalobeanu's most impactful contribution is his work on benchmarking robotic indoor navigation, exemplified by his 2015 paper "An Experimental Protocol for Benchmarking Robotic Indoor Navigation" (31 citations), which provides a standardized framework for evaluating navigation performance. He further advanced the field by developing practical mapping and navigation solutions using the low-cost Microsoft Kinect sensor, demonstrating that inexpensive hardware can achieve reliable performance in large indoor environments (16 citations). Jalobeanu also introduced an innovative, inexpensive method for evaluating localization accuracy using printed landmarks instead of expensive external sensors (14 citations), making performance assessment more accessible to smaller labs. His recent work explores semi-supervised language interfaces for robot instruction following (2023), aiming to reduce the data burden for natural language control. Through these contributions, Jalobeanu has helped democratize robotics research by lowering the cost and complexity of both navigation systems and their evaluation.