  This paper introduces an active object detection and localization framework
that combines a robust untextured object detection and 3D pose estimation
algorithm with a novel next-best-view selection strategy. We address the
detection and localization problems by proposing an edge-based registration
algorithm that refines the object position by minimizing a cost directly
extracted from a 3D image tensor that encodes the minimum distance to an edge
point in a joint direction/location space. We face the next-best-view problem
by exploiting a sequential decision process that, for each step, selects the
next camera position which maximizes the mutual information between the state
and the next observations. We solve the intrinsic intractability of this
solution by generating observations that represent scene realizations, i.e.
combination samples of object hypothesis provided by the object detector, while
modeling the state by means of a set of constantly resampled particles.
Experiments performed on different real world, challenging datasets confirm the
effectiveness of the proposed methods.
