A Self-Calibration Method for Bore-Sight Error of Ground-Based Mobile Mapping System

Xiangang, 孟宪刚 Meng; Shaoxing, 胡少兴 Hu; Aiwu, 张爱武 Zhang; Yihao, 段乙好 Duan; Xiao, 张晓 Zhang

doi:10.3788/cjl201441.1108008

Cited by 1 publication

(2 citation statements)

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“…Sensor calibration mainly includes feature point adjustment back-calculation, feature surface fitting solution, round-trip vertical wall surface solution and other methods (Yao, Wang and Sun 2016). Some scholars used a large number of point pairs to carry out optimal parameter settlement according to the spatial relationship between a large number of control points and their corresponding point clouds in the calibration field (Meng et al 2014). Besides, Hu (2011) applied surface features to carry out final parameter determination and establish error equations according to laser point clouds with errors and plane equations, carry out correction iteration, and then eliminate the influence of errors.…”

Section: Overview Methodologymentioning

confidence: 99%

“…However, the calibration mentioned above method for the multi-sensors system has some defects. For example, some methods need to deploy too many control points (Shi et al 2018;Meng et al 2014;Fan et al 2019), which takes a long time and cannot meet the requirements of fast and efficient calibration. Other methods calibrate the multisensor system by combining LiDAR point cloud and plane to establish error equations (Hu 2011), which do not take into account the loss of precision in plane feature fitting.…”

Section: Overview Methodologymentioning

confidence: 99%

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

Guo

Zhou

Zhao

et al. 2021

Geo-spatial Information Science

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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.

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Section: Overview Methodologymentioning

confidence: 99%

Section: Overview Methodologymentioning

confidence: 99%