  Spectral methods such as the improved Fourier Mellin Invariant (iFMI)
transform have proved faster, more robust and accurate than feature based
methods on image registration. However, iFMI is restricted to work only when
the camera moves in 2D space and has not been applied on omni-cameras images so
far. In this work, we extend the iFMI method and apply a motion model to
estimate an omni-camera's pose when it moves in 3D space. This is particularly
useful in field robotics applications to get a rapid and comprehensive view of
unstructured environments, and to estimate robustly the robot pose. In the
experiment section, we compared the extended iFMI method against ORB and AKAZE
feature based approaches on three datasets showing different type of
environments: office, lawn and urban scenery (MPI-omni dataset). The results
show that our method boosts the accuracy of the robot pose estimation two to
four times with respect to the feature registration techniques, while offering
lower processing times. Furthermore, the iFMI approach presents the best
performance against motion blur typically present in mobile robotics.
