Multi-wavelength laser line profile sensing for agricultural applications

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Titel: Multi-wavelength laser line profile sensing for agricultural applications
Autor(en): Strothmann, Wolfram
Erstgutachter: Prof. Dr. Joachim Hertzberg
Zweitgutachter: Prof. Dr. Arno Ruckelshausen
Prof. Dr. Hans-Werner Griepentrog
Zusammenfassung: This dissertation elaborates on the novel sensing approach of multi-wavelength laser line profiling (MWLP). It is a novel sensor concept that expands on the well-known and broadly adopted laser line profile sensing concept for triangulation-based range imaging. Thereby, the MWLP concept does not just use one line laser but multiple line lasers at different wavelengths scanned by a single monochrome imager. Moreover, it collects not only the 3D distance values but also reflection intensity and backscattering of the laser lines are evaluated. The system collects spectrally selective image-based data in an active manner. Thus, it can be geared toward an application-specific wavelength configuration by mounting a set of lasers of the required wavelengths. Consequently, with this system image-based 3D range data can be collected along with reflection intensity and backscattering data at multiple, selectable wavelengths using just a single monochrome image sensor. Starting from a basic draft of the idea, the approach was realized in terms of hardware and software design and implementation. The approach was shown to be feasible and the prototype performed well as compared with other state-of-the-art sensor systems. The sensor raw data can be visualized and accessed as overlayed distance images, point clouds or mesh. Further, for selected example applications it was demonstrated that the sensor data gathered by the system can serve as descriptive input for real world agricultural classification problems. The sensor data was classified in a pixel-based manner. This allows very flexible, quick and easy adaptation of the classification toward new field situations.
Schlagworte: Laser triangulation; Spectral analysis; 3D sensing; Image processing; Classification; Agricultural sensing; Machine learning; Model adaptation
Erscheinungsdatum: 3-Nov-2016
Lizenzbezeichnung: Namensnennung 3.0 Unported
URL der Lizenz:
Publikationstyp: Dissertation oder Habilitation [doctoralThesis]
Enthalten in den Sammlungen:FB06 - E-Dissertationen

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