Learning Visual Landmarks for Pose Estimation

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Learning Visual Landmarks for Pose Estimation
Abstract-- We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features called image-domain landmarks. The landmark learning mechanism is designed to be applicable to a wide range of environments. Each landmark is detected as a local extremum of a measure of uniqueness and represented by an appearance-based encoding. Localization is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Quantitative experimental evidence is presented that demonstrates that accurate pose estimates can be obtained, despite changes to the environment.
Robert Sim, Gregory Dudek
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where ICRA
Authors Robert Sim, Gregory Dudek
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