Location-based services is an upcoming market providing location specific information to the user. While GPS and cellular networks help in rough localization outdoors, they cannot provide directional information and exact position. Cameras do not require any external signals and work almost everywhere. However, the data dimensionality of images makes the data association (matching) along multiple frames hard. We show in this talk that we can solve the visual localization problem without matching with a voting scheme.
Voting of the parameter space (known as Hough transform) can be accelerated if we realize that the transform integral is a convolution integral, not necessarily on the image plane. Using tools from harmonic analysis on groups, we give a new light to Fourier methods and propose algorithms that are robust and suitable when the majority of features are outliers. We close the talk with a new and fully autimatic method for registering to eachother range scans with limited overlap.