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A Target-Based Self-Calibration Method for Terrestrial Laser Scanners and its Robust Solution

Fundamental systematic errors in point cloud data are inevitable due to a variety of factors, ranging from the external environment during scanning or observation by a terrestrial laser scanner (TLS) to the assembly of the instrument. For low-cost scanners, error terms may be further accentuated and included, in addition to systematic errors, random or even serious errors that directly affect the coordinates of each point in the point cloud, which are directly related to the quality of the point cloud data and subsequent processing. To address the above issues, we attempted to propose a robust target-based self-calibration method for TLS at the algorithmic level without considering the network design and measurement configuration, and derived its solution by normalizing the residual vector and calculating an equivalent covariance matrix based on the IGG III function. After validating the simulated and measured data, the experimental results showed that the proposed self-calibration method could effectively eliminate the random and gross errors associated with the observations; improved the accuracy of the points from centimeter to millimeter level; and increased the accuracy of the corrected checkpoints by 58%, 47%, and 33%, respectively, compared to the three existing methods. However, the proposed method was unable to take into account the attenuation of parameter correlations and further refinement in terms of measurement configurations would be subsequently required.