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Publication - Professor Walterio Mayol-Cuevas

    Discovering Planes and Collapsing the State Space in Visual SLAM


    Gee, A, D, C, Mayol-Cuevas, W & Calway, A, 2007, ‘Discovering Planes and Collapsing the State Space in Visual SLAM’. in: 18th British Machine Vision Conference.


    Recent advances in real-time visual SLAM have been based primarily on mapping isolated 3-D points. This presents difficulties when seeking to extend operation to wide areas, as the system state becomes large, requiring increasing computational effort. In this paper we present a novel approach to this problem in which planar structural components are embedded within the state to represent mapped points lying on a common plane. This collapses the state size, reducing computation and improving scalability, as well as giving a higher level scene description. Critically, the plane parameters are augmented into the SLAM state in a proper fashion, maintaining inherent uncertainties via a full covariance representation. Results for simulated data and for real-time operation demonstrate that the approach is effective.

    Full details in the University publications repository