Computer Vision

Permanent URI for this community

https://hdl.handle.net/2440/15000

The Computer Vision Group was formed in 1991 and has since grown substantially. The group has undertaken a number of projects on the analysis and understanding of images and video.

Current major projects:

  • 3D modelling from video
  • automated video surveillance.

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Browsing Computer Vision by Author "Chen, B."

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    Deep learning for 2D scan matching and loop closure
    (IEEE, 2017) Li, J.; Zhan, H.; Chen, B.; Reid, I.; Lee, G.; IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (24 Sep 2017 - 28 Sep 2017 : Vancouver, Canada); Bicchi, A.; Okamura, A.
    Although 2D LiDAR based Simultaneous Localization and Mapping (SLAM) is a relatively mature topic nowadays, the loop closure problem remains challenging due to the lack of distinctive features in 2D LiDAR range scans. Existing research can be roughly divided into correlation based approaches e.g. scan-to-submap matching and feature based methods e.g. bag-of-words (BoW). In this paper, we solve loop closure detection and relative pose transformation using 2D LiDAR within an end-to-end Deep Learning framework. The algorithm is verified with simulation data and on an Unmanned Aerial Vehicle (UAV) flying in indoor environment. The loop detection ConvNet alone achieves an accuracy of 98.2% in loop closure detection. With a verification step using the scan matching ConvNet, the false positive rate drops to around 0.001%. The proposed approach processes 6000 pairs of raw LiDAR scans per second on a Nvidia GTX1080 GPU.
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    Satellite pose estimation with deep landmark regression and nonlinear pose refinement
    (IEEE, 2019) Chen, B.; Cao, J.; Parra Bustos, A.; Chin, T.; IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (27 Oct 2019 - 2 Nov 2019 : Seoul, South Korea)
    We propose an approach to estimate the 6DOF pose of a satellite, relative to a canonical pose, from a single image. Such a problem is crucial in many space proximity operations, such as docking, debris removal, and inter-spacecraft communications. Our approach combines machine learning and geometric optimisation, by predicting the coordinates of a set of landmarks in the input image, associating the landmarks to their corresponding 3D points on an a priori reconstructed 3D model, then solving for the object pose using non-linear optimisation. Our approach is not only novel for this specific pose estimation task, which helps to further open up a relatively new domain for machine learning and computer vision, but it also demonstrates superior accuracy and won the first place in the recent Kelvins Pose Estimation Challenge organised by the European Space Agency (ESA).