Image analysis – Pattern recognition – Feature extraction
Reexamination Certificate
2004-12-22
2010-11-09
Le, Brian Q (Department: 2624)
Image analysis
Pattern recognition
Feature extraction
C382S103000
Reexamination Certificate
active
07831094
ABSTRACT:
Simultaneous localization and mapping (SLAM) utilizes multiple view feature descriptors to robustly determine location despite appearance changes that would stifle conventional systems. A SLAM algorithm generates a feature descriptor for a scene from different perspectives using kernel principal component analysis (KPCA). When the SLAM module subsequently receives a recognition image after a wide baseline change, it can refer to correspondences from the feature descriptor to continue map building and/or determine location. Appearance variations can result from, for example, a change in illumination, partial occlusion, a change in scale, a change in orientation, change in distance, warping, and the like. After an appearance variation, a structure-from-motion module uses feature descriptors to reorient itself and continue map building using an extended Kalman Filter. Through the use of a database of comprehensive feature descriptors, the SLAM module is also able to refine a position estimation despite appearance variations.
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Gupta Rakesh
Meltzer Jason
Yang Ming-Hsuan
Duell Mark E.
Fenwick & West LLP
Honda Motor Co. Ltd.
Le Brian Q
Park Edward
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