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About

Joacim Dybedal is a researcher specializing in 3D sensing, computer vision, and industrial robotics perception systems. His work focuses on the development of scalable, distributed sensor networks capable of mapping and monitoring large-scale industrial environments in real time — a challenge of growing importance as automation and robotics continue to reshape manufacturing. Dybedal's most significant contributions center on multi-sensor calibration and 3D point cloud processing. His 2019 paper on automatic RGB-D camera network calibration using retroreflective ArUco markers and the Iterative Closest Point (ICP) algorithm — his most cited work with 18 citations — introduced a non-invasive, robust approach that considerably simplifies the deployment of complex sensor arrays. Complementing this, his research on embedded processing and octree-based compression of point cloud data addresses the practical challenge of efficiently transferring large volumes of 3D sensor data across networks. His earlier benchmark study on visual marker-guided point cloud registration in industrial robot cells (12 citations) and his architecture for distributed static 3D sensor nodes (11 citations) together establish a coherent body of work that bridges sensor fusion, embedded systems, and real-world industrial deployment — making his research particularly valuable for engineers and roboticists working on intelligent factory environments.