Template gradient matching in spherical images

Abstract

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.