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A probabilistic model for appearance-based robot localization
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DOI：10.1016/S0262-8856(00)00086-X.png)
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In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estimate this density model from the data with a kernel estimation method. We show how the parameters of the model influence the localization performance. We also study how many features and which features are needed for good localization. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
robot localization
feature extraction
probabilistic modeling
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A probabilistic approach to concurrent mapping and localization for mobile robots.png)
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