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About

Atle Aalerud is a researcher specializing in 3D sensor networks, industrial environment perception, and human-robot collaboration. His work centers on developing robust, scalable systems for real-time monitoring and safety in large-scale industrial robotic environments, addressing critical challenges at the intersection of computer vision, embedded systems, and robotics. Among his most significant contributions is a suite of methods for calibrating and deploying multi-sensor RGB-D networks in industrial settings. His 2019 paper on automatic calibration using retroreflective ArUco markers and the ICP scheme (18 citations) has become a foundational reference in the field, complemented by earlier benchmark work on visual marker-guided point cloud registration (12 citations). He has also pioneered scalable embedded solutions for processing and compressing 3D point cloud data, enabling practical deployment across expansive factory floors. Aalerud's research extends into human safety, developing real-time collision detection systems and sophisticated motion tracking algorithms — including an augmented Kalman filter approach robust to sensor occlusion — that support safe human-robot collaboration. His 2020 investigation into perception latency highlights his attention to safety-critical system parameters often overlooked by peers. With a growing citation record across seven notable publications, Aalerud represents an emerging voice in industrial 3D perception and intelligent robotic safety systems.