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Thomas Dall Larsen is a pioneering researcher in mobile robotics, with a primary focus on state estimation and sensor fusion for autonomous navigation. His most influential work centers on the design and evaluation of Kalman filters for mobile robot localization, a cornerstone of modern robotics. In his highly cited 2003 paper (65 citations), Larsen systematically compared kinematic and odometric approaches to Kalman filter design, providing critical insights into the trade-offs between model complexity and estimation accuracy. This work remains a foundational reference for engineers developing robust localization systems. Larsen also made significant contributions to odometry calibration, introducing autocalibration methods to correct systematic errors in wheel-base and encoder gain estimates—a practical solution that enhances long-term robot reliability. His research on sensor management for identity fusion and adaptive Kalman filtering in the presence of uncertainties further demonstrates his commitment to improving real-world robot performance under challenging conditions. While his citation counts reflect a focused, technical audience, Larsen’s work has directly influenced the design of countless mobile robot platforms, particularly in industrial and research settings where precise, reliable navigation is essential.