Self-localization plays a fundamental role in all the activities of a Service Mobile Robot, from simple point-to-point navigation to complex fetch-and-carry tasks. In particular, in presence of an environment which changes dynamically, a trade-off must be found between apparently opposite characteristics: uniqueness (i.e. the ability to univocally recognize every location in the environment) and ductility (i.e. the ability to recognize a location of the environment in spite of small changes). The paper shows a vision-based approach which exploits color analysis and clustering to match perceptions with a pre-stored model of the environment, and relies on a Markovian model to update a probability density over the possible robot's configurations.
Markov-Localization through Color Features Comparison
CASTELNOVI, MATTIA;SGORBISSA, ANTONIO;ZACCARIA, RENATO UGO RAFFAELE
2004-01-01
Abstract
Self-localization plays a fundamental role in all the activities of a Service Mobile Robot, from simple point-to-point navigation to complex fetch-and-carry tasks. In particular, in presence of an environment which changes dynamically, a trade-off must be found between apparently opposite characteristics: uniqueness (i.e. the ability to univocally recognize every location in the environment) and ductility (i.e. the ability to recognize a location of the environment in spite of small changes). The paper shows a vision-based approach which exploits color analysis and clustering to match perceptions with a pre-stored model of the environment, and relies on a Markovian model to update a probability density over the possible robot's configurations.
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