Data-Driven Learning for Calibrating Galvanometric Laser Scanners

• Published in: IEEE sensors journal Vol. 15; no. 10; pp. 5709 - 5717
• Main Authors: Wissel, Tobias, Wagner, Benjamin, Stuber, Patrick, Schweikard, Achim, Ernst, Floris
• Format: Journal Article
• Language: English
• Published: IEEE 01.10.2015
• Subjects: Calibration; Cameras; data-driven calibration; galvanometric laser scanner; Gaussian Processes; Kernel; Laser modes; Laser theory; Neural Networks; statistical learning; Support Vector regression; Table lookup; Three-dimensional displays; support vector regression; statistical learning; data-driven calibration; Galvanometric laser scanner; neural networks; Gaussian processes
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2015.2447835

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.