Ronald Clark
Ronnie Clark
Dyson Research Fellow
Department of Computing
Imperial College London
Email: ronald.clark{at}imperial.ac.uk; ron.clark{at}live.com; LinkedIn,Twitter,DBLP

About me

I am a postdoctoral fellow at Imperial College London where I currently hold a Dyson Fellowship. I obtained my PhD from the University of Oxford Department of Computer Science. I am interested in the general topic of visual machine perception which is needed to enable mobile devices to model, explore and understand their surroundings.

I am particularly interested in ways in which deep neural models can be used alongside traditional methods and existing domain knowledge. My current work focusses on how recurrent, convolutional neural networks can be used to create consistent, dense, semantically annotated reconstructions of the world. I also have a keen interest in computer graphics and animation.

In the past I have worked on machine learning for natural user interaction [8,9] and optimal systems design [7]. I got my MSc degree in Information Engineering from the University of Witwatersrand, South Africa, at the Centre for Systems and Control in 2014.  

New Updates

  • Aug, 2018: Invited Talk at the London Machine Learning Meetup. Video here!
  • July, 2018: New papers accepted at 3DV, ECCV, BMVC and IEEE TMC.
  • June, 2018: CVPR Best Paper Honourable Mention award!
  • May, 2018: Excited to be on the BMVC technical programme committee.
  • Feb, 2018: One paper accepted at CVPR 2018.
  • Jan, 2018: Organizing the 1st Workshop on Deep Learning for Visual SLAM at CVPR'18.
  • Aug, 2017: One paper accepted at International Journal of Robotics Research
  • April, 2017: Excited to be joining the Dyson Lab at Imperial College.
  • March, 2017: Talk at the ORI/AIMS Mobile Autonomy Workshop.



Publications

optimization-robotics-computer vision
Fusion++: Volumetric Object-Level SLAM
John McCormac*, Ronald Clark*, Michael Bloesch, Stefan Leutenegger, Andrew J. Davison
International Conference on 3D Vision (3DV'18) [pdf, video] *equal contribution
optimization-robotics-computer vision
Learning to Solve Non-Linear Least-Squares for Monocular Stereo
Ronald Clark, Michael Bloesch,Jan Czarnowski, Stefan Leutenegger, Andrew J. Davison
European Conference on Computer Vision (ECCV'18) [pdf, bibtex]
optimization-robotics-computer vision
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Wenbin Li, Sajad Saeedi, John McCormac, Ronald Clark, Dimos Tzoumanikas, Qing Ye, Yuzhong Huang, Rui Tang, Stefan Leutenegger
British Machine Vision Conference (BMVC'18) [pdf,project]
optimization-robotics-computer vision
CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM
Michael Bloesch,Jan Czarnowski, Ronald Clark, Stefan Leutenegger, Andrew J. Davison
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'18) (Best Paper runner-up) [pdf,video]
optimization-robotics-computer vision
Meta Learning for Instance-Level Data Association
Ronald Clark, John McCormac, Stefan Leutenegger, Andrew J. Davison
Neural Information Processing Systems (NIPS'17) Workshop on MetaLearning [pdf,bibtex]
optimization-robotics-computer vision
Efficient Indoor Positioning with Visual Experiences via Lifelong Learning
Hongkai Wen, Ronald Clark, Sen Wang, Xiaoxuan Lu, Bowen Du, Wen Hu, Niki Trigoni
IEEE Transactions on Mobile Computing (TMC) [pdf,bibtex]
optimization-robotics-computer vision
3D Object Reconstruction from a Single Depth View with Adversarial Learning
Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni
IEEE International Conference on Computer Vision Workshops (ICCV-W) [pdf,bibtex]
optimization-robotics-computer vision
A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'17) [pdf,bibtex]
optimization-robotics-computer vision
End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks
Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
International Journal of Robotics Research (IJRR). [pdf]
optimization-robotics-computer vision
VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
The 31st AAAI Conference on Artificial Intelligence (AAAI) [Project,pdf,bibtex]
optimization-robotics-computer vision
DeepVO: Towards End-to-End Visual Odometry with Recurrent Convolutional Networks
Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
IEEE International Conference on Robotics and Automation (ICRA), 2017. [pdf]
optimization-robotics-computer vision
Large Scale Indoor Keyframe-based Localization using Geomagnetic Field and Motion Pattern
Sen Wang, Hongkai Wen, Ronald Clark, Andrew Markham, Niki Trigoni
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. [pdf,bibtex]
optimization-robotics-computer vision
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016. [pdf,bibtex]
optimization-robotics-computer vision
Pushing the Limits of Indoor 3D Modelling using Mobile Sensing
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni
submitted to IEEE Trans. on Mobile Computing (TMC), 2016. [pdf]
optimization-robotics-computer vision
Robust Vision-based Indoor Localization
Ronald Clark, Andrew Markham, Niki Trigoni
IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2015. [pdf,bibtex]
optimization-intelligent-system
Optimization of a Hybrid Energy System through Fast Convex Programming
Ronald Clark,W.A. Cronje, Anton van Wyk
IEEE International Conference on Intelligent Systems Modelling and Simulation (ISMS), 2014. (Best paper nominee 14/103) [pdf,bibtex]
optimization-robotics-computer vision
System for the Recognition of Handwritten Mathematical Expressions
Ronald Clark,Quik Kung, Anton van Wyk
IEEE International Conference on Computer as a Tool (EUROCON), 2013. (Nominated for SPC) [pdf,bibtex]
classifier-support-vector-regression
Recognising Handwritten Mathematical expressions using an Ensemble of SVM Classifiers
Ronald Clark,Quik Kung, Anton van Wyk
13th Symposium of the Pattern Recognition Association of South Africa (PRASA/IAPR), 2012. [pdf,bibtex]

Theses

optimization-robotics-computer vision
Visual-Inertial Odometry, Mapping and Relocalization through Learning
Ronald Clark
PhD Thesis (DPhil), University of Oxford (June 2017) [pdf,bibtex]

Code

machine learning, deep learning, svr, slam, 3D reconstruction, artificial intelligence
MATLAB Support Vector Regression code
Ronald Clark
MATLAB FIle Exchange, 2013 [link]. A modiefied version of the code has been

Journal and Conference Review Service

IROS 2017
ICRA 2017
Pattern Recognition Letters

Projects Contributed to

Mobile Robotics: Enabling a Pervasive Technology of the Future (EP/M019918/1)

Description: "Robotics is fast advancing to a point where autonomous systems can add real value to the public domain. The potential reach of mobile robotics in particular is vast, covering sectors as diverse as transport, logistics, space, defence, agriculture and infrastructure management. In order to realise this potential we need our robots to be cheap, work synergistically with people in large, complex and time-changing environments and do so robustly for long periods of time." [link]

Teaching

Computer Animation , University of Oxford, (2014,2015) [course page]
- transformation chains, scene description languages, time-varying transformations, interpolation functions, collision detection, physical response models
ELEN4017 Network Fundamentals , University of Witwatersrand [course page]
- basic routing protocols, dynamic name servers, address resolution, network stack, worl-wide web

Original Theme by: Weilin Huang