  Algorithms for robotic visual search can benefit from the use of visual
attention methods in order to reduce computational costs. Here, we describe how
three distinct mechanisms of visual attention can be integrated and
productively used to improve search performance. The first is viewpoint
selection as has been proposed earlier using a greedy search over a
probabilistic occupancy grid representation. The second is top-down
object-based attention using a histogram backprojection method, also previously
described. The third is visual saliency. This is novel in the sense that it is
not used as a region-of-interest method for the current image but rather as a
noncombinatorial form of look-ahead in search for future viewpoint selection.
Additionally, the integration of these three attentional schemes within a
single framework is unique and not previously studied. We examine our proposed
method in scenarios where little or no information regarding the environment is
available. Through extensive experiments on a mobile robot, we show that our
method improves visual search performance by reducing the time and number of
actions required.
