Papers
12
Total Citations
111
H-Index
6
About
Matthew Gadd is a leading researcher in autonomous vehicle navigation, specializing in radar-based perception, visual localisation, and robust long-term autonomy for mobile robots. His work addresses critical challenges in enabling robots to operate reliably in diverse and challenging environments, from warehouses to off-road terrains. Gadd’s major contributions include pioneering constant-curvature motion constraints for radar odometry, which refines data associations for non-holonomic robots, and developing a graph-based framework for infrastructure-free warehouse navigation using only monocular cameras. He also introduced version control concepts for fleet-wide visual localisation, allowing vehicles to share and update visual experiences for sustained autonomy. His highly cited papers, such as “What Goes Around” (20 citations) and “A Framework for Infrastructure-Free Warehouse Navigation” (20 citations), underscore his impact. Gadd’s notable achievements include creating the Oxford Offroad Radar Dataset (OORD) to support research in off-road environments and designing “The Hulk,” a weather-proof vehicle for long-term outdoor autonomy. His recent work on RAG-Driver explores explainable AI for autonomous decision-making, highlighting his commitment to trustworthy robotics. With over 100 citations across his top papers, Gadd is shaping the future of resilient, explainable autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2A framework for infrastructure-free warehouse navigation20 citations · 2015
- 3Checkout my map: Version control for fleetwide visual localisation18 citations · 2016
- 4<i>OORD</i>: The Oxford Offroad Radar Dataset13 citations · 2024
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