Scalable Monocular SLAM

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Scalable Monocular SLAM
Localization and mapping in unknown environments becomes more difficult as the complexity of the environment increases. With conventional techniques, the cost of maintaining estimates rises rapidly with the number of landmarks mapped. We present a monocular SLAM system that employs a particle filter and top-down search to allow realtime performance while mapping large numbers of landmarks. To our knowledge, we are the first to apply this FastSLAM-type particle filter to single-camera SLAM. We also introduce a novel partial initialization procedure that efficiently determines the depth of new landmarks. Moreover, we use information available in observations of new landmarks to improve camera pose estimates. Results show the system operating in real-time on a standard workstation while mapping hundreds of landmarks.
Ethan Eade, Tom Drummond
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Ethan Eade, Tom Drummond
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