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.
- Apr 17: I'm Chair of the Virtual Futures track at the VR World Congress, happening in Bristol from 11th-13th April 2017.
- Mar 17: Shuda Li presented our RGB-D SLAM system at the BMVA Technical Meeting on Analysis and Processing of RGBD Data
- Feb 17: I gave a keynote talk at the International Workshop on Vision and Control for Autonomous Drones held at the National Institute for astrophysics, Optics and Electronics (INAOE) in Puebla, Mexico.
- Dec 16: Congratulations to Shuda Li, who successfully defended his PhD thesis on 15 Dec 2016. Well done Shuda!
- Nov 16: Pilailuck Panphattarasap is presenting our paper on LDD place recognition at ACCV 2016.
- Oct 16: I'll be presenting our work on HDRFusion at 3DV in Stanford, 25-28 Oct.
- Sept 16: Shuda Li and I will be demonstrating our RGB-D relocalisation and HDRFusion systems at ECCV 2016 in Amsterdam in early October.
- Sept 16: New paper on HDRFusion with Shuda Li, Ankar Handa and Yang Zhang accepted for 3DV 2016. I'll be presenting the paper in Stanford in late October. Pre-print: HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor, Shuda Li, Ankur Handa, Yang Zhang and Andrew Calway, arXiv:1604.00895.
- Aug 16: New paper on LDD place recognition with Pilailuck Panphattarasap accepted for ACCV 2016: (pre-print) Visual place recognition using landmark distribution descriptors, Pilailuck Panphattarasap and Andrew Calway, arXiv:1608.04274.
- Feb 16: Shuda Li has released the source code and data for our paper Absolute pose estimation using multiple forms of correspondences from RGB-D frames to be presented at ICRA 2016. GitHub repository.
- Jan 16: New paper with Shuda Li - Absolute pose estimation using multiple forms of correspondences from RGB-D frames - accepted for presentation at ICRA 2016.
Research Assistants and Students
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.
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.