Most of today's robot vehicles are equipped with omnidirectional
sensors which provide surround awareness and easier navigation.
Due to the persistence of the appearance in omnidirectional images,
many global navigation or formation control tasks, instead of using
landmarks or fiducials, they need only reference images of target
positions or objects. In this paper, we study the problem of template
matching in spherical images. The natural transformation of a pattern
on the sphere is a 3D rotation and template matching is the
localization of a target in any orientation given by a reference
image. Unfortunately, the support of the template is space variant on
the Euler angle parameterization. Here we propose a new method
which matches the gradients of the
image and the template, with space-invariant operation.
Using properties of the angular momentum, we have proved
in fact that the gradient correlation can be very easily computed by the
3D Inverse Fourier Transform of a linear combination of spherical
harmonics. An exhaustive search localizes the maximum of this
correlation. Experimental results on real data show a very accurate
localization with a variety of targets. In future work, we plan to
address targets appearing in different scales.