We want robots to be able to navigate autonomously in an unknown environment, using as the principal source of data a vision system. A single camera can solve problems in an indoor environment, when the robot moves on a plane surface. Visually guided systems often use artificial landmarks, while more advanced ones rely on natural landmarks.
The self-location problem is important when the robot has to move in autonomous way. Dead reckoning reduces the location error, but is unable to keep the error under a given threshold. When used in telecontrol the self location is again important: the robot starts from any position, builds a map with any origin, and has to match the new map with the standard one shown in the user interface.
Autonomy in a moderately dynamic system using vision is here developed with new algorithms based on the use of a single camera. The map is constructed from image interpretation, and computing the transformation from a local to a global map solves auto localization. The same approach is developed with data arising from laser pointing, with the difference that the segmentation gives results less ambiguous than in camera images.
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Landmark-based localization
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Globally unique localization
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Positioning beacon systems
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Route-based localization
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