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• W197766211 abstract "by EMILY AMANDA WARREN This thesis explores the use of machine learning in the context of autonomous mobile robots driving on roads, with the focus on improving the robot’s internal map. Early chapters cover the mapping efforts of DEXTER, Team Case’s entry in the 2007 DARPA Urban Challenge. Competent driving may include the use of a priori information, such as road maps, and online sensory information, including vehicle position and orientation estimates in absolute coordinates as well as error coordinates relative to a sensed road. An algorithm may select the best of these typically flawed sources, or more robustly, use all flawed sources to improve an uncertain world map, both globally in terms of registration corrections and locally in terms of improving knowledge of obscured roads. It is shown how unsupervised learning can be used to train recognition of sensor credibility in a manner applicable to optimal data fusion." @default.
• W197766211 created "2016-06-24" @default.
• W197766211 creator A5040818867 @default.
• W197766211 date "2008-01-01" @default.
• W197766211 modified "2023-09-27" @default.
• W197766211 title "MACHINE LEARNING FOR ROAD FOLLOWING BY AUTONOMOUS MOBILE ROBOTS" @default.
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• W197766211 hasPublicationYear "2008" @default.
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