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 new approach to attentive exploration 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 view-point saliency map obtained according to transportation processes. Interesting features emerging from pre-attentive pop-outs are projected on the current metric map and, according the preference engendered, 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.
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