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Autonomous Attentive Exploration in Search and Rescue Scenarios
"... Abstract. In task-oriented exploration a robot has to direct its sight and delving towards the most promising regions of the environment, according to the task, in order to optimize its search. If the goal is dynamically set on the basis of what it is perceived, attention plays a crucial role, as it ..."
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Abstract. In task-oriented exploration a robot has to direct its sight and delving towards the most promising regions of the environment, according to the task, in order to optimize its search. If the goal is dynamically set on the basis of what it is perceived, attention plays a crucial role, as it allows to combine fast glancing with accurate analysis, enabling the robot to quickly jump to conclusion by selecting the interesting spots in the environment requiring a further analysis. We present a task-oriented attentive exploration system designed for an autonomous rover working in rescue scenarios. The visual-attention process combined with the simultaneous localization and mapping one guides the robot search through an incremental generation of a global saliency map obtained according to transportation processes. Interesting features diffuse streams of particles warming up those map areas they pass through, in so generating hot regions that result in optimal vantage points for the robot to observe the salient spots glanced at while searching. We show the effectiveness of the approach by providing experimental results and comparisons. 1
An Overt Visual Attention Mechanism Based on Saliency Dynamics
, 2007
"... A visual overt attention mechanism is presented in this paper. Our algorithm chooses the next fixation point for a robot mobile camera in order to track several objects around the robot simultaneously, even if the whole set of them can not be covered by the same camera image. Our approach is based o ..."
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A visual overt attention mechanism is presented in this paper. Our algorithm chooses the next fixation point for a robot mobile camera in order to track several objects around the robot simultaneously, even if the whole set of them can not be covered by the same camera image. Our approach is based on two related measurements, liveliness and saliency, that dynamically evolve depending on the image observations and the camera movements. The attention is shared among exploring the surroundings for salient features, reobserving the tracked objects and other task-dependent points to look at. A winner-takes-all competition provides a flexible time sharing behavior with natural appearances of new objects, disappearances and inhibition of return. It also accepts top down features to look for and different priorities for them. Several experiments have been carried out with a real Pioneer robot endowed with mobile camera and are also described.
