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About

Daniel Eck is a researcher specializing in mobile robotics, autonomous navigation, and the application of robotic technologies to cultural heritage preservation. His most recognized contributions center on the use of mobile robots for 3D mapping of archaeological and cultural heritage sites, with his 2015 paper "Robotic Mapping of Cultural Heritage Sites" accumulating 25 citations — a testament to its influence in bridging robotics and archaeology. Using the mobile robot Irma3D, Eck developed and evaluated intelligent processes for generating high-fidelity 3D environmental models from large and complex datasets, addressing one of the field's most pressing data-processing challenges. Beyond heritage documentation, Eck has made meaningful contributions to autonomous robot navigation, including the development of RRTCAP*, a novel trajectory planning algorithm that simultaneously integrates motion planning and execution on real robotic platforms. His work also extends to outdoor localization, where he designed evaluation frameworks for benchmarking positioning algorithms using high-precision systems. Additionally, his research into Unscented Kalman Filter (UKF) sensor fusion further demonstrates his commitment to robust robot localization. Across his body of work, Eck consistently advances the practical deployment of autonomous robots in real-world, complex environments.