本文提出一种Monte-Carlo匹配与定位算法，基于已知地理参考影像实现了地面车载全景影像序列的精确定位。首先，基于贝叶斯准则和马尔科夫随机链，推导了几何、辐射两种约束条件下运动影像序列全局定位的通用统计模型。然后，顾及阴影、遮挡、动态目标等困难条件下的多源影像匹配80%的误匹配率，基于粒子滤波原理提出Monte-Carlo匹配与定位一体化求解算法（MIML），通过预测、更新的迭代策略，在剔除粗差的同时获得最佳定位结果。通过2000余张车载全景影像序列的定位实验，验证了本方法能够克服多源影像匹配中误匹配点太多导致的传统平差算法无法收敛的问题，实现了车载全景序列影像的精确定位。

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.