By J. Torreao
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Extra info for Advances in Stereo Vision
In addition, we have proposed methods for robot pose estimation as well as for global metric adjustment in loop closings. The set of perceptual and high-level behaviors needed to solve the active modeling problem are organized according to our attention-based control architecture. In this architecture, attention is conceived as an intermediary between visual perception and action control, solving two fundamental behavioral questions for the robot: where to look and what to do. Using this scheme, we have deﬁned the different attentional and high-level behaviors that allow the robot to solve the modeling task in an autonomous way.
The Active Modeler behavior starts the task by gaining access to the visual information around the robot. For this purpose, it activates an attentional selector, which attends to visual regions of interest situated in front of the robot, and starts turning the robot base around. The rotational velocity varies according to the attentional response in such a way that the speed increases if no visual region is perceived in front of the robot. Once the robot returns to its initial orientation, a ﬁrst model of the room is obtained.
This is a very useful representation for the robot to effectively move around man-made environments. For instance, the robot could analyze the graph to obtain the minimum path connecting any two rooms. Moreover, this representation can be extended using recursive descriptions to express more complex world structures like buildings. Thus, a building could be represented by a node containing several interconnected subgraphs. Each subgraph would represent a ﬂoor of the building and contain a description of the interconnections between the different rooms and corridors in it.