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About

Graham Riley is a prominent computer scientist whose research sits at the intersection of computer vision, robotics, and high-performance embedded computing. His work focuses primarily on Simultaneous Localization and Mapping (SLAM), visual odometry, and the challenge of deploying computationally intensive real-time scene understanding systems on resource-constrained hardware platforms. Riley's most significant contributions include advancing the field of real-time dense computer vision for robotics and augmented/virtual reality applications, where he has helped define both the algorithmic and architectural challenges of running SLAM on low-power embedded systems. His development of the SLAMBench evaluation framework — now in its third iteration — has been particularly influential, providing the research community with a systematic, reproducible methodology for objectively comparing SLAM systems, a contribution that has earned over 37 citations and shaped how researchers benchmark progress in the field. His 2018 survey navigating the SLAM landscape has accumulated 51 citations, reflecting its value as a reference for practitioners across robotics and immersive technology domains. More recently, Riley has explored hardware acceleration techniques, including high-level synthesis for visual odometry, pushing toward practical deployment on platforms such as drones and VR headsets. His body of work has collectively garnered over 200 citations, cementing his reputation as a key voice in efficient spatial computing research.