Real-Time Simultaneous Localisation and Mapping with a Single Camera

Ego-motion estimation for an agile single camera moving through general, unknown scenes becomes a much more challenging problem when real-time performance is required rather than under the off-line processing conditions under which most successful structure from motion work has been achieved. This task of estimating camera motion from measurements of a continuously expanding set of selfmapped visual features is one of a class of problems known as Simultaneous Localisation and Mapping (SLAM) in the robotics community, and we argue that such real-time mapping research, despite rarely being camera-based, is more relevant here than off-line structure from motion methods due to the more fundamental emphasis placed on propagation of uncertainty. We present a top-down Bayesian framework for singlecamera localisation via mapping of a sparse set of natural features using motion modelling and an informationguided active measurement strategy, in particular addressing the difficult issue of real-...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Andrew J. Davison
• Keywords: Computer Vision, ICCV 2003, Permits Robust Localisation, Real-time Localisation, Real-time Mapping Research, Simultaneous Localisation, Sparse Prior Scene