AUTOMATIC IN-SITU SELF-CALIBRATION OF A PANORAMIC TLS FROM A SINGLE STATION USING 2D KEYPOINTS
Keywords: Self-Calibration, Laser Scanning, TLS, Point Cloud, Intensity Image, Feature Detector, Accuracy
Abstract. Terrestrial laser scanner (TLS) measurements are unavoidably affected by systematic influences due to internal misalignments. The magnitude of the resulting errors can exceed the magnitude of random errors significantly deteriorating the quality of the obtained point clouds. Hence, the task of calibrating TLSs is important for applications with high demands regarding accuracy. In recent years, multiple in-situ self-calibration approaches were derived allowing the successful estimation of up-to-date calibration parameters. These approaches rely either on using manually placed targets or on using man-made geometric objects found in surroundings. Herein, we widen the existing toolbox with an alternative approach for panoramic TLSs, for the cases where such prerequisites cannot be met. We build upon the existing target-based two-face calibration method by substituting targets with precisely localized 2D keypoints, i.e. local features, detected in panoramic intensity images using the Förstner operator. To overcome the detriment of the perspective change on the feature localization accuracy, we estimate the majority of the relevant calibration parameters from a single station. The approach is verified on real data obtained with the Leica ScanStation P20. The obtained results were tested against the affirmed target-based two-face self-calibration. Analysis proved that the estimated calibration parameters are directly comparable both in the terms of parameter precision and correlation. In the end, we employ an effective evaluation procedure for testing the impact of the calibration results on the point cloud quality.