In this thesis we investigate how monocular image measurements can be used as the single source of information for a vehicle to sense and navigate through its surroundings. First we investigate what is the subset of vehicle location, environment mapping and vehicle motion that can be retrieved from images only. In particular, results apply to the case where no model of the vehicle, nor odometry or acceleration measurements are available. Then, we investigate the use of the information that can be extracted from images on visual servoing tasks and we define a servoing approach, named {\em Appearance Servoing}, that explicitly imposes the existing control constraints in the navigation of an Appearance Map. Finally, we present an experimental study case of the use of appearance where a sequence of images is used to construct a simple topological map of an office environment and then navigate a robot within it.
Isn't Appearance Enough? - Nonlinear Observability and Observers for Appearance Localization, Mapping, Motion Reconstruction and Servoing Problems and their application to Vehicle Navigation
2011
Abstract
In this thesis we investigate how monocular image measurements can be used as the single source of information for a vehicle to sense and navigate through its surroundings. First we investigate what is the subset of vehicle location, environment mapping and vehicle motion that can be retrieved from images only. In particular, results apply to the case where no model of the vehicle, nor odometry or acceleration measurements are available. Then, we investigate the use of the information that can be extracted from images on visual servoing tasks and we define a servoing approach, named {\em Appearance Servoing}, that explicitly imposes the existing control constraints in the navigation of an Appearance Map. Finally, we present an experimental study case of the use of appearance where a sequence of images is used to construct a simple topological map of an office environment and then navigate a robot within it.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/130317
URN:NBN:IT:UNIPI-130317