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About

Markus Hillemann is a computer vision and robotics researcher whose work sits at the intersection of geometric calibration, uncertainty quantification, and autonomous robot perception. He is best known for his contributions to hand–eye calibration of vision-guided industrial robots, where his 2021 and 2023 papers — accumulating 27 citations combined — introduced a statistically principled framework that explicitly models robot uncertainty, addressing a critical gap left by traditional calibration methods that overlooked absolute accuracy limitations. Building on this foundation, Hillemann has expanded into deep learning-based 6D object pose estimation, applying deep ensembles to quantify uncertainty in high-stakes scenarios such as human–robot interaction and industrial automation. His 2024 work in this area has already attracted 6 citations, reflecting growing community interest. More recently, he has explored neural radiance fields for 3D scene reconstruction in robotic settings and efficient multi-task learning combining semantic segmentation with monocular depth estimation. His research on semantic mapping further demonstrates a commitment to enabling autonomous mobile robots in real production environments. Across these directions, Hillemann's unifying theme is making robotic perception not only accurate but reliably trustworthy — an increasingly vital quality as robots operate in safety-critical industrial settings.