Learning to Localize with Gaussian Process Regression on Omnidirectional Image Data

We present a probabilistic localization and orientation estimation method for mobile agents equipped with omnidirectional vision. In our appearance-based framework, a scene is learned in an offline step by modeling the variation of the image energy in the frequency domain via Gaussian process regression. The metric localization of novel views is then solved by maximizing the joint predictive probability of the Gaussian processes using a particle filter which allows to incorporate a motion model in the prediction step. Based on the position estimate, a synthetic view is generated and used as a reference for the orientation estimation which is also performed in the Fourier space. Using real as well as virtual data, we show that this framework allows for robust localization in 2D and 3D scenes based on very low resolution images and with competitive computational load.