2023 - Research.com Computer Science in United Kingdom Leader Award
2017 - Fellow of the Royal Academy of Engineering (UK)
His main research concerns Artificial intelligence, Computer vision, RGB color model, Structure from motion and Simultaneous localization and mapping. Artificial intelligence is closely attributed to Computer graphics in his study. When carried out as part of a general Computer vision research project, his work on Monocular, Feature and 3D reconstruction is frequently linked to work in Point, therefore connecting diverse disciplines of study.
His biological study spans a wide range of topics, including Stereopsis and Image sensor. His studies deal with areas such as Ground truth, Segmentation and Pose as well as RGB color model. His Structure from motion research integrates issues from Filter and Bundle adjustment.
His primary areas of investigation include Artificial intelligence, Computer vision, Robot, Simultaneous localization and mapping and Robotics. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Pattern recognition. His Computer vision study combines topics from a wide range of disciplines, such as Computer graphics, Mobile robot and Visual odometry.
His Robot study combines topics in areas such as Active vision and Human–computer interaction. The Simultaneous localization and mapping study combines topics in areas such as Algorithm and Image sensor. His research in Augmented reality intersects with topics in Object, Probabilistic logic and Iterative reconstruction.
Andrew J. Davison spends much of his time researching Artificial intelligence, Computer vision, Representation, Human–computer interaction and Robotics. His research ties Machine learning and Artificial intelligence together. His Computer vision research focuses on Process and how it relates to Spiking neural network.
Andrew J. Davison combines subjects such as Image classifier, Surface element, Projection and Surfel with his study of Representation. His Human–computer interaction research includes themes of Domain, Robot learning, Variety, Robot and Reinforcement learning. Andrew J. Davison works mostly in the field of Simultaneous localization and mapping, limiting it down to topics relating to Iterative reconstruction and, in certain cases, View based, as a part of the same area of interest.
Artificial intelligence, Human–computer interaction, Robot learning, Computer vision and Monocular are his primary areas of study. His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning. His biological study deals with issues like Robot, which deal with fields such as Leverage, Ai systems and Embodied cognition.
His work carried out in the field of Robot learning brings together such families of science as Domain, Imitation learning and Reinforcement learning. Andrew J. Davison has researched Computer vision in several fields, including Process and Representation. His Monocular study integrates concerns from other disciplines, such as Probabilistic logic, Prior probability and Set.
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MonoSLAM: Real-Time Single Camera SLAM
A.J. Davison;I.D. Reid;N.D. Molton;O. Stasse.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
KinectFusion: Real-time dense surface mapping and tracking
Richard A. Newcombe;Shahram Izadi;Otmar Hilliges;David Molyneaux.
international symposium on mixed and augmented reality (2011)
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Shahram Izadi;David Kim;Otmar Hilliges;David Molyneaux.
user interface software and technology (2011)
DTAM: Dense tracking and mapping in real-time
Richard A. Newcombe;Steven J. Lovegrove;Andrew J. Davison.
international conference on computer vision (2011)
Real-Time Simultaneous Localisation and Mapping with a Single Camera
Andrew J. Davison.
international conference on computer vision (2003)
Pablo Fern;ndez Alcantarilla;Adrien Bartoli;Andrew J. Davison.
european conference on computer vision (2012)
Inverse Depth Parametrization for Monocular SLAM
J. Civera;A.J. Davison;J. Montiel.
IEEE Transactions on Robotics (2008)
Simultaneous localization and map-building using active vision
A.J. Davison;D.W. Murray.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
Renato F. Salas-Moreno;Richard A. Newcombe;Hauke Strasdat;Paul H. J. Kelly.
computer vision and pattern recognition (2013)
A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
Ankur Handa;Thomas Whelan;John McDonald;Andrew J. Davison.
international conference on robotics and automation (2014)
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