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Discovering Planes and Collapsing the State Space in Visual SLAM

Andrew P. Gee, Denis Chekhlov, Walterio Mayol, Andrew Calway, Discovering Planes and Collapsing the State Space in Visual SLAM. Proceedings of the 18th British Machine Vision Conference. September 2007. PDF, 440 Kbytes. External information

Abstract

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.

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