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Journal of Applied Remote Sensing • Open Access

Improved bore-sight calibration for airborne light detection and ranging using planar patches
Author(s): Dong Li; Huadong Guo; Cheng Wang; Pinliang Dong; Zhengli Zuo

Paper Abstract

Airborne light detection and ranging (LiDAR) system calibration is a crucial procedure for ensuring the accuracy of point data. A common practice is to use conjugate planar patches to recover systematic parameters based on coplanar constraints and to use planes with different orientations to decrease the correlations between the systematic errors. When there are not sufficient planar patches and the configuration of planar patches is not optimal, it is difficult to guarantee the reliability of the estimated system parameters. Based on the analyses of the bore-sight angle effects, we find that not only the orientations but also the distribution of planar patches play an important role in the calibration procedure. We propose an improved method for bore-sight calibration based on the principles of symmetry of coordinate offsets and low correlations between bore-sight angles. Comparisons of the experimental results of bore-sight angle calibration suggest that the proposed configuration of conjugate planar patches can decrease the correlations between bore-sight angles and improve the reliability of calibration results. The optical results obtained from four gable-roof buildings are very close to the results calculated by the RiProcess software with a deviation of about 0.001 deg.

Paper Details

Date Published: 9 May 2016
PDF: 17 pages
J. Appl. Remote Sens. 10(2) 024001 doi: 10.1117/1.JRS.10.024001
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Dong Li, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Insitute of Electronics, CAS (China)
Huadong Guo, Institute of Remote Sensing and Digital Earth (China)
Cheng Wang, Institute of Remote Sensing and Digital Earth (China)
Pinliang Dong, Univ. of North Texas (United States)
Zhengli Zuo, Institute of Remote Sensing and Digital Earth (China)

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