Calibration of Galvanometric Laser Scanners Using Statistical Learning Methods

Stefan Lüdtke, Benjamin Wagner, Ralf Bruder, Patrick Stüber, Floris Ernst, Achim Schweikard, Tobias Wissel


Galvanometric laser scanners can be used for optical tracking. Model-based calibration of these systems is inaccurate and not adaptable to variations in the system. Therefore, a calibration method based on statistical learning methods is presented which directly incorporates the triangulation problem. We investigate linear regression as well as Artificial Neural Networks. The results are validated using (1) the cross-validated prediction accuracy within the calibration space, and (2) plane reconstruction accuracy. All statistical learning methods outperformed the model-based approach leading to an improvement of up to 74% for the cross-validated 3D root-mean-square error and 70-74% for the plane reconstruction. While the neural network achieved mean errors below 0.5 mm, the linear regression results suggest a good compromise between accuracy and computational load.
Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2015
EditorsHeinz Handels, Thomas Martin Deserno, Hans-Peter Meinzer, Thomas Tolxdorff
Number of pages6
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Publication date25.02.2015
ISBN (Print)978-3-662-46223-2
ISBN (Electronic)978-3-662-46224-9
Publication statusPublished - 25.02.2015
EventWorkshops on Image Processing for Medicine 2015: Algorthim-Systems-Applications - Lübeck, Germany
Duration: 15.03.201517.03.2015


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