Talks and Poster Presentations (with Proceedings-Entry):

J. Schlarp, E. Csencsics, G. Schitter:
"Feature detection and scan area selection for 3D laser scanning sensors";
Talk: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Auckland (New Zealand); 07-09-2018 - 07-12-2018; in: "2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics", (2018), 6 pages.

English abstract:
Due to the trend towards small lot sizes and fast
changes of relevant product features in production processes,
the demands for flexible measurement systems with high precision
and throughput are constantly growing. By using optical
scanning sensor systems, e.g. comprising a laser triangulation
sensor and a fast steering mirror, the demands on speed
and precision can be partially satisfied, leaving the flexibility
of adapting to various measurement tasks to be solved. A
common use-case is the need to measure a certain feature
on a sample with a higher spatial resolution than the rest
of the sample. By using machine vision such features can be
detected automatically, enabling an automated adaptation of the
scanning system to the measurement task. This work presents
the combination of an optical scanning sensor system with an
agglomerative clustering algorithm for detecting features and
their dimensions. Based on the identified features, offset and
scan amplitudes for high resolution rescans can be derived,
resulting in a flexible metrology tool. Experimental results show
that several individual features on a sample can be precisely
detected and that an automatically parametrized rescan can
significantly increase the lateral resolution of the acquired

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.