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About

Martin Dekan is a robotics researcher whose work spans mobile robot navigation, human-robot interaction, and depth sensor evaluation. Over the course of his career, he has made significant contributions to both foundational robotics algorithms and the practical assessment of sensing technologies, establishing himself as a key voice in applied robotics research. Dekan's early work focused on mobile robot navigation, including modifications to Vector Field Histogram (VFH) methods, optimal path planning, and laser rangefinder applications — research that collectively garnered over 100 citations and laid groundwork for reactive and autonomous navigation systems. His 2014 study on optimizing robotic arm trajectories using genetic algorithms (46 citations) and his 2017 work on pointing-gesture-based human-robot interaction (54 citations) further broadened his contributions across manipulation and intuitive robot control. Perhaps his most impactful contributions are his rigorous evaluations of Microsoft's Kinect sensor family. His 2021 papers comparing Kinect V1, V2, and Azure Kinect — examining depth precision, noise characteristics, and skeleton tracking accuracy — have collectively accumulated over 350 citations, becoming essential references for researchers deploying depth sensors in robotics, rehabilitation, and motion capture applications. His work exemplifies a commitment to empirical, practically grounded research that bridges hardware evaluation and real-world robotic systems.