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CVPR
2004
IEEE

Visual Odometry and Map Correlation

15 years 1 months ago
Visual Odometry and Map Correlation
In this paper, we study how estimates of ego-motion based on feature tracking (visual odometry) can be improved using a rough (low accuracy) map of where the observer has been. We call the process of aligning the visual ego-motion with the map locations as map correlation. Since absolute estimates of camera position are unreliable, we use stable local information such as change in orientation to perform the alignment. We also detect when the observer's path has crossed back on itself, which helps improve both the visual odometry estimates and the alignment between the video and map sequences. The final alignment is computed using a graphical model whose MAP estimate is inferred using loopy belief propagation. Results are presented on a number of indoor and outdoor sequences.
Anat Levin, Richard Szeliski
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Anat Levin, Richard Szeliski
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