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[VIRTUAL] GRASP On Robotics: Andrew Davison, Imperial College London, “Towards Graph-Based Spatial AI”

October 23, 2020 @ 11:00 am - 12:30 pm


To enable the next generation of smart robots and devices which can truly interact with their environments, Simultaneous Localisation and Mapping (SLAM) will progressively develop into a vision-driven geometric and semantic `Spatial AI’ perception capability which gives devices the real-time dynamic model in which to reason in intuitive and intelligent ways about their actions.  A fundamental issue is the algorithmic architecture which will enable estimation and machine learning components to come together to enable efficient, incremental updating of scene representation, and I believe that graph strucures where storage and computation come together will be the key. New computing and sensing hardware is now becoming available which makes research in this direction a reality.


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Andrew Davison is Professor of Robot Vision and Director of the Dyson Robotics Laboratory at Imperial College London. His long-term research focus is on SLAM (Simultaneous Localisation and Mapping) and its evolution towards general `Spatial AI’: computer vision algorithms which enable robots and other artificial devices to map, localise within and ultimately understand and interact with the 3D spaces around them. With his research group and collaborators he has consistently developed and demonstrated breakthrough systems, including MonoSLAM, KinectFusion, SLAM++ and CodeSLAM, and recent prizes include Best Paper at ECCV 2016 and Best Paper Honourable Mention at CVPR 2018. He has also had strong involvement in taking this technology into real applications, in particular through his work with Dyson on the design of the visual mapping system inside the Dyson 360 Eye robot vacuum cleaner and as co-founder of applied SLAM start-up SLAMcore. He was elected Fellow of the Royal Academy of Engineering in 2017.


October 23, 2020
11:00 am - 12:30 pm
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