Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Surface inspection planning for laser line scanners

: Mohammadikaji, M.

Volltext urn:nbn:de:0011-n-4175756 (755 KByte PDF)
MD5 Fingerprint: 7fe43edad47d8cdaefac72d09766217d
(CC) by-sa
Erstellt am: 25.10.2016

Beyerer, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe; Karlsruhe Institute of Technology -KIT-, Lehrstuhl für Interaktive Echtzeitsysteme -IES-:
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2015. Proceedings : July, 19 to 26, Triberg-Nussbach, Germany
Karlsruhe: KIT Scientific Publishing, 2016 (Karlsruher Schriften zur Anthropomatik 24)
ISBN: 978-3-7315-0519-8
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2015, Triberg-Nussbach>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

The important role of automated visual methods in industrial product inspection necessitates the design of optimized and precise measurement setups. Due to the high dimensionality of the design space, the manual choice of the geometrical and optical parameters is tedious and often not optimal. In this article we study the problem of inspection planning for laser line scanners which are affordable and widely-used inspection tools. To this end, the measurement model is defined and appropriate evaluation metrics are introduced, which formulate the optimization problem in terms of a number of constrains and cost functions. Visibility analysis, lateral resolution, range resolution, and the measurement uncertainty are of the main metrics we cover. Computer graphics simulations are utilized to simulate the measurement in different setup configurations and estimate the evaluation metrics. We also propose a general uncertainty model which can be applied for modeling the uncertainty in laser scanners. The optimum laser scanner setup can be achieved by optimizing the defined evaluation metrics using a multi-objective approach.