Global Color Image Features for Discrete Self-localization of an Indoor Vehicle

In autonomous indoor navigation some number of localizations and orientations of the vehicle can be learned in advance. No artificial landmarks are required to exist. We describe and compare the detection of several global features of color images (sensor data). This constitutes the measurement process in a self-localization approach that is based on Bayes filtering of a Markov environment – the posterior probability density over possible discrete robot locations (the belief) is recursively computed. The approach was tested to provide robust results under varying scene brightness conditions and small measurement errors.

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