Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

• Main Authors: Ming Guo, Yuquan Zhou, Jianghong Zhao, Tengfei Zhou, Bingnan Yan, Xianfeng Huang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-06-01
• Series: Geo-spatial Information Science
• Subjects: mobile mapping system (mms); close-range photogrammetry; international gnss service (igs); mms calibration; accuracy evaluation
• Online Access: http://dx.doi.org/10.1080/10095020.2021.1924084

The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measurement accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relationship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation parameters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision requirements of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as autonomous driving, digital twin city, urban brain et al.