  One of the central problems in computer vision is the detection of
semantically important objects and the estimation of their pose. Most of the
work in object detection has been based on single image processing and its
performance is limited by occlusions and ambiguity in appearance and geometry.
This paper proposes an active approach to object detection by controlling the
point of view of a mobile depth camera. When an initial static detection phase
identifies an object of interest, several hypotheses are made about its class
and orientation. The sensor then plans a sequence of views, which balances the
amount of energy used to move with the chance of identifying the correct
hypothesis. We formulate an active hypothesis testing problem, which includes
sensor mobility, and solve it using a point-based approximate POMDP algorithm.
The validity of our approach is verified through simulation and real-world
experiments with the PR2 robot. The results suggest that our approach
outperforms the widely-used greedy view point selection and provides a
significant improvement over static object detection.
