Data-driven learning for calibrating galvanometric laser scanners

Tobias Wissel, Benjamin Wagner, Patrick Stüber, Achim Schweikard, Floris Ernst

5 Citations (Scopus)


State-of-the-art calibration very often constructs models motivated by a real-world device. Recently, artificial neural networks (ANNs) have been proposed as a more universal, accurate, and practical black-box approach. For a galvanometric triangulation device based on two mirrors, we embrace this proposal and set it into context with other supervised data-driven approaches: 1) ridge regression; 2) support vector regression; and 3) Gaussian processes. We show that they outperform available model-based approaches and yield similar performance compared with a memorizing lookup table calibration. The results demonstrate that an off-the-shelf usage of ANNs may run into generalization problems. Restricting the space of functions using kernel-based learning has proven to be advantageous. Finally, all approaches and distinct properties are discussed in a broader context, since each application entails differently relevant requirements for its calibration. This also holds for any calibration other than the considered triangulation device.

Original languageEnglish
Article number7128690
JournalIEEE Sensors Journal
Issue number10
Pages (from-to)5709-5717
Number of pages9
Publication statusPublished - 01.10.2015


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