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    Internal misalignments in lab-built terrestrial laser scanners (TLS) cause systematic errors. This study calibrates these errors, improving point-cloud accuracy using an angle measurement error model and self-calibration methods.

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    Area of Science:

    • Geomatics Engineering
    • Metrology
    • Optical Engineering

    Background:

    • Terrestrial laser scanners (TLS) are crucial for 3D data acquisition.
    • Internal misalignments in lab-built TLS introduce systematic errors, degrading point-cloud positional accuracy.
    • Calibration is essential to enhance the reliability of TLS measurements.

    Purpose of the Study:

    • To develop and validate a self-calibration method for lab-built terrestrial laser scanners (TLS).
    • To identify and model the primary systematic errors affecting TLS positional accuracy.
    • To improve the horizontal and vertical accuracy of TLS-derived point clouds.

    Main Methods:

    • Established an angle measurement error model using ray-tracing, incorporating five types of mounting angle errors.
    • Employed the two-face and network methods for estimating angle measurement errors.
    • Validated the self-calibration method through experimental analysis.

    Main Results:

    • Identified mirror tilt error and vertical index offset error as the dominant systematic errors in lab-built TLS.
    • Demonstrated significant improvement in point-cloud positional accuracy in both horizontal and vertical directions.
    • The proposed method effectively reduces systematic errors originating from internal misalignments.

    Conclusions:

    • The developed self-calibration method significantly enhances the positional accuracy of lab-built TLS point clouds.
    • Mirror tilt and vertical index offset errors are key systematic errors requiring calibration.
    • The methodology is adaptable for calibrating other TLS systems by modifying the angle measurement error model.