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 self-mapped
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 single-camera localisation via mapping of a sparse set of natural features using motion modelling and an information-guided active measurement strategy, in particular addressing the difficult issue of real-time feature initialisation via a factored sampling approach. Real-time handling of uncertainty permits robust localisation via the creating and active measurement of a sparse map of landmarks such that regions can be re-visited after periods of neglect and localisation can continue through periods when few features are visible. Results are presented of real-time localisation for a
hand-waved camera with very sparse prior scene knowledge and all processing carried out on a desktop PC.
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