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Orientation in Manhattan: Equiprojective Classes and Sequential Estimation
 

Summary: Orientation in Manhattan: Equiprojective
Classes and Sequential Estimation
Andre´ T. Martins,
Pedro M.Q. Aguiar, Member, IEEE, and
Ma´rio A.T. Figueiredo, Sr. Member, IEEE
Abstract--The problem of inferring 3D orientation of a camera from video
sequences has been mostly addressed by first computing correspondences of
image features. This intermediate step is now seen as the main bottleneck of those
approaches. In this paper, we propose a new 3D orientation estimation method for
urban (indoor and outdoor) environments, which avoids correspondences between
frames. The scene property exploited by our method is that many edges are
oriented along three orthogonal directions; this is the recently introduced
Manhattan world (MW) assumption. The main contributions of this paper are: the
definition of equivalence classes of equiprojective orientations, the introduction of a
new small rotation model, formalizing the fact that the camera moves smoothly,
and the decoupling of elevation and twist angle estimation from that of the compass
angle. We build a probabilistic sequential orientation estimation method, based on
an MW likelihood model, with the above-listed contributions allowing a drastic
reduction of the search space for each orientation estimate. We demonstrate the
performance of our method using real video sequences.

  

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

 

Collections: Engineering