I am a Reader in Computer Vision at the University of Bristol based in the Department of Computer Science and a member of the Visual Information Laboratory (VIL) and the Bristol Robotics Laboratory (BRL). My research covers computer vision and its applications - robotics, wearable computing and augmented reality - and I have done a lot of work on 3-D tracking and scene reconstruction, mainly in simultaneous localisation and mapping (SLAM). Working with industry and on interdisciplinary projects is always a high priority for me - please get in touch if you are interested in working with me. More details can be found below and in my publications.

Contact details: Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK; T: +44 117 9545149; E: andrew at cs dot bris dot ac dot uk.

News

• May 18: New vacancy for Research Associate in Computer Vision and Sensor Fusion to join our Innovate UK project with Perceptual Robotics. This is a unique opprtunity to join an exciting project to develop an inspection system for offshore wind turbines using drones. The RA will be responsible for developing drone localisation and landing capability wrt a USV using vision and other sensors. Further details and applications.
• Apr 18: New Innovate UK project starting with Perceptual Robotics on inspection of offshore wind turbines using drones, part of the UK Industrial Strategy Challenge Fund for Robotics and AI in Extreme Environments. We will be hiring a Research Associate for this project very soon - more information to follow shortly.
• Mar 18: New paper on arXiv: Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors, with Pilailuck Panphattarasap, describes novel approach to localisation wrt a 2-D map using image data and semantic modelling.
• Mar 18: New paper on arXiv: Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation, with Oliver Moolan-Feroze, describes using a CNN approach to predict out of view feeature points for robust 'up close' 3-D tracking.
• Feb 18: NEW: PhD studentship available in Augmented Reality, starting October 2018. Full details are here.
• June 17: I'm organising the International Workshop on Lines, Planes and Manhattan Models for 3-D Mapping (LPM 2017) along with Michael Kaess and Sri Ramalingam. The workshop is taking place at IROS on September 28, 2017 in Vancouver.

Research Assistants and Students

• Research Assistants: Oliver Moolan-Feroze.
• Research Students: Pilailuck Panphattarasap; Xingrui Yang.
• Alumni: Tom Banwell; John Bartholomew; Jose Martinez-Carranza; Dima Damen; Jorge Escamilla-Ambrosio; Rob Frampton; Osian Haines; Shuda Li; Stefan Kruger; Mark Pupilli; David Tweed; Annie Yao.

I enjoy working with people who want to discover, innovate and work hard with others to make things happen. If you are interested in working with me then please get in touch. If you want to do a PhD then I'd be happy to hear from you but please take a look at what I do and how we might work together before you contact me. If you are looking for funding, then any vacancies I have will be advertised on this page; otherwise, you may like to consider the various scholarships offered by the University.

Projects

	HDRFUSION: RGB-D SLAM WITH AUTO EXPOSURE RGB-D SLAM system which is robust to appearance changes caused by RGB auto exposure and is able to fuse multiple exposure frames to build HDR scene reconstructions. Results demonstrate high tracking reliability and reconstructions with far greater dynamic range of luminosity.
	LDD PLACE RECOGNITION Place recognition using landmark distribution descriptors (LDD) which encode the spatial organisation of salient landmarks detected using edge boxes and represented using CNN features. Results demonstrate high accuracy for highly disparate views in urban environments.
	MULTI-CORRESPONDENCE 3-D POSE ESTIMATION Novel algorithm for estimating the 3-D pose of an RGB-D sensor which uses multiple forms of correspondence - 2-D, 3-D and surface normals - to gain improved performance in terms of accuracy and robustness. Results demonstrate significant improvement over existing algorithms.
	RGB-D RELOCALISATION USING PAIRWISE GEOMETRY fast and robust relocalisation in an RGB-D SLAM system based on pairwise 3-D geometry of key points encoded within a graph type structure combined with efficient key point representation based on octree representation. results demonstrate that the relocalisation out performs that of other approaches.