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Sensor processing for mobile robot localization, exploration and navigation

Robert Mandelbaum-1996-10-03-Scholarly Commons (University of Pennsylvania)
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TL;DRAbstract

In the context of a mobile robotic agent, we describe a unified framework for the competencies of localization, navigation, exploration and map-building. In this work, we focus on localization. We describe the design, implementation, testing and evaluation of data-processing algorithms for three sensor modalities: ultrasound, stereo vision and patterned light. We develop a sensor model for each modality. In each case, distinctive features of objects in the field of view are extracted. In each case, the output of the algorithm is of a form which facilitates integration within the framework, and hence the localization of the agent in a partially-known environment. We delineate a computationally efficient method for sensor-based localization of a mobile robot based on planar features extracted using ultrasound. The method runs in time linear in the number of detected features, both for establishing correspondences between extracted and map features, and for pose estimation. We outline the

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In the context of a mobile robotic agent, we describe a unified framework for the competencies of localization, navigation, exploration and map-building. In this work, we focus on localization. We describe the design, implementation, testing and evaluation of data-processing algorithms for three sensor modalities: ultrasound, stereo vision and patterned light. We develop a sensor model for each modality. In each case, distinctive features of objects in the field of view are extracted. In each case, the output of the algorithm is of a form which facilitates integration within the framework, and hence the localization of the agent in a partially-known environment. We delineate a computationally efficient method for sensor-based localization of a mobile robot based on planar features extracted using ultrasound. The method runs in time linear in the number of detected features, both for establishing correspondences between extracted and map features, and for pose estimation. We outline the

Keywords

Mobile robotSimultaneous localization and mappingComputer scienceArtificial intelligenceComputer visionKalman filterRoboticsContext (archaeology)

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