A Monte-Carlo georegistration method (MCG) is presented to solve the global localization problem of a ground mobile vehicle with car-mounted panoramic sequential imagery and geo-referenced ortho-images. Firstly, a general stochastic localization model is deduced according to Bayes rules and Markov chain under the two constraints of geometry and radiance. Then a particle filtering method called Monte-Carlo is introduced to solved the localization model, considering the difficulties of multi-source matching between pano-images and ortho-images caused by shadows, occlusions, moving objects etc., and achieves the matching and geo-referencing simultaneously. A localization test with more than 2000 pano-images and one ortho-image with 0.25m accuracy proved that MCG can tolerate excessive blunders more than 80% caused by mismatching and obtain a high localization accuracy approaching BA results with full GCPs.