Application of particle filters to vision-based orientation estimation using harmonic analysis

We investigate the application of particle filters to estimate the orientation of a mobile agent based on an omnidirectional video stream. By applying spherical signal analysis to sequences of low resolution input images we perform a real-time estimation of the relative camera rotation. We use normalized cross-correlation computed with a fast frequency domain approach that yields unbiased estimates which can be further processed by a particle filter. We discuss the quaternion representation of the rotational state space and evaluate methods for meaningful averaging. A prototype system is presented and experiments with real image sequences show that robust estimation of the rotational motion is achievable even when the input images are corrupted, e.g. due to occlusions.