Andrew Davison holds the position of Professor of Robot Vision at the Department of Computing, Imperial College London, and leads the Dyson Robotics Laboratory at Imperial College where he is working on vision and AI technology for next generation home robotics. He also leads the Robot Vision Research Group though most of his activity is now within the Dyson Lab.

He is working in computer vision and robotics: specifically his main research has concerned SLAM (Simultaneous Localisation and Mapping) using vision, with a particular emphasis on methods that work in real-time with commodity cameras. He pioneered SLAM with vision from the mid 1990s onwards, and brought the SLAM acronym and methods from robotics to single camera computer vision with the breakthrough MonoSLAM algorithm in 2003 which enabled long-term, drift-free, real-time SLAM from a single camera for the first time, inspiring many researchers and industry developments in robotics and inside-out tracking for VR and AR.

Currently his main research interests are in improving the performance in terms of dynamics, scale, detail level, efficiency and semantic understanding of real-time 3D vision. He believes that SLAM is evolving into something even more important that he is calling "Spatial AI". Please read his 2018 discussion paper FutureMapping: The Computational Structure of Spatial AI Systems and the 2019 follow-up FutureMapping 2: Gaussian Belief Propagation for Spatial AI for an up-to-date insight into his current thinking.