Joseph DeGol

University of Illinois Urbana-Champaign, Microsoft Research (United Kingdom), University of Illinois System

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Total Citations

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About

Joseph DeGol is a leading researcher at the intersection of computer vision, robotics, and augmented reality, with a focus on fast and reliable spatial perception. His most impactful contribution is **ChromaTag** (2017, 14 citations), a novel fiducial marker system that uses opponent colors for rapid false-positive rejection, significantly accelerating detection for AR and robotics. He further advanced pose estimation with **gDLS*** (2020, 12 citations), a generalized solver that efficiently estimates camera pose and scale while incorporating gravity and scale priors—critical for real-time 3D mapping and multi-camera systems. In robotics, DeGol designed a passive cam-follower mechanism (2015, 8 citations) enabling quadrotors to autonomously pick up and release payloads without active control. His most recent work (2024) introduces a neurosymbolic approach to adaptive feature extraction in SLAM, blending learned representations with symbolic reasoning for robust tracking in dynamic environments. With a consistent record of high-impact, application-driven research, DeGol’s work directly enables safer autonomous navigation and more immersive mixed-reality experiences.