Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes

文献类型： 外文期刊

第一作者： Ji, Shunping

作者： Ji, Shunping;Yuan, Xiuxiao;Shi, Yun;Shao, Xiaowei;Yang, Peng;Wu, Wenbin;Tang, Huajun;Shi, Yun;Shibasaki, Ryosuke;Shan, Jie;Shao, Xiaowei;Shi, Zhongchao

作者机构：

关键词： Mobile mapping;Particle filtering;Monte Carlo;Kalman filtering;Image matching;Gross errors

期刊名称：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING （ 影响因子：8.979； 五年影响因子：9.948 ）

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收录情况： SCI

摘要： Georeferencing image sequences is critical for mobile mapping systems. Traditional methods such as bundle adjustment need adequate and well-distributed ground control points (GCP) when accurate GPS data are not available in complex urban scenes. For applications of large areas, automatic extraction of GCPs by matching vehicle-born image sequences with geo-referenced ortho-images will be a better choice than intensive GCP collection with field surveying. However, such image matching generated GCP's are highly noisy, especially in complex urban street environments due to shadows, occlusions and moving objects in the ortho images. This study presents a probabilistic solution that integrates matching and localization under one framework. First, a probabilistic and global localization model is formulated based on the Bayes' rules and Markov chain. Unlike many conventional methods, our model can accommodate non-Gaussian observation. In the next step, a particle filtering method is applied to determine this model under highly noisy GCP's. Owing to the multiple hypotheses tracking represented by diverse particles, the method can balance the strength of geometric and radiometric constraints, i.e., drifted motion models and noisy GCP's, and guarantee an approximately optimal trajectory. Carried out tests are with thousands of mobile panoramic images and aerial ortho-images. Comparing with the conventional extended Kalman filtering and a global registration method, the proposed approach can succeed even under more than 80% gross errors in GCP's and reach a good accuracy equivalent to the traditional bundle adjustment with dense and precise control. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

分类号： P2

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