Abstract
Mosaic-based positioning is a paradigm for the simultaneous construction of a photo-mosaic as a visual map, and its use to achieve accurate positioning. We discuss the application of a novel fusion principle, the so-called an extended covariance intersection (ECI), for addressing the mosaic-based positioning as a data fusion problem. The covariance intersection (CI) principle has been proposed for the fusion of highly correlated data. In contrast to the extended Kalman filter (EKF), the drawback is the conservative nature of the solution, as the extend of correlation becomes insignificant. The primary advantage of ECI, by decomposing the estimates from information sources into both dependent and independent components, is to arrive at improved estimates, neither as over-optimistic as from an EKF, nor as over-conservative as the CI solution. Experiments with real data are presented to evaluate the performance of the proposed ECI-based formulation.
