3D-Metrology

3D-Metrology enables a precise geometric description and provides highly accurate positioning and orientation in the three-dimensional space and in time.

For applications of mobile measurement technology we use the latest laser tracker, total stations, laser scanner, measuring arms and industrial cameras in combination with custom developments in hard- and software.

Our applications range from very small objects such as Specimens for nana tomography of complex technical components to larger installations such as telescopes, antennas and measuring campaigns with respect to global reference systems such as e.g. the establishment of laser communication units to low-flying satellites.

Contact Person

Prof. Dr.-Ing. Anita Sellent

Tel.: +49 6131-628-
Fax.: +49 6131-628-9

Prof. Dr.-Ing. Frank Boochs

Tel.: +49 6131-628-1432
Fax.: +49 6131-628-91432

Prof. Dr.-Ing. Jörg Klonowski

Tel.: +49 6131-628-1436
Fax.: +49 6131-628-91436

Prof. Dr.-Ing. Martin Schlüter

Tel.: +49 6131-628-1440
Fax.: +49 6131-628-91440

Prof. Dr. Ing. Renate Czommer

Tel.: +49 6131-628-1439
Fax.: +49 6131-628-91439

Projects

other projects »

Publications

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i3mainz - Jahresbericht 2018

2019

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Jahrebericht 2018

Im Jahresbericht werden die Projekte und Aktivitäten des i3mainz in komprimierter Form vorgestellt.

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Berührungslose Positionsbestimmung von spiegelnden Kugeln mit Methoden des maschinellen Sehens

2019

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zfv - Zeitschrift für Geodäsie, Geoinformation und Langmanagement

Die Raumstrecken zwischen den Mittelpunkten spiegelnder Kugeln werden mit einem theodolitbasierten Industriemesssystem hochgenau bestimmt. Dabei werden die Theodolitokulare durch je eine Adapteroptik samt Industriekamera ersetzt. So kommt ein ausschließlich auf Methoden des Machine Vision aufbauender, automatisierter Workflow zum Einsatz, bei dem die Zielpunktdefinition und die einzelnen Theodolitzielungen erfolgreich voneinander entkoppelt werden, ähnlich wie bei der automatischen Zielpunkterkennung von geodätischen Tachymetern auf Vermessungsreflektoren.

The spatial distances between the centers of reflecting spheres are precisely determined using a theodolite-based industrial measuring system. The theodolite eyepieces are each replaced by an adapter optic including an industrial camera. For the first time, an automated workflow based exclusively on Machine Vision methods is used, in which the target definition and the individual theodolite targetings are successfully decoupled from each other, similar to the automatic target
recognition of geodetic tachymeters towards survey reflectors.

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Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes

2019

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ISPRS International Journal of Geo-Information

In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

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Connected Semantic Concepts as a Base for Optimal Recording and Computer-Based Modelling of Cultural Heritage Objects

2019

RTF

Structural Analysis of Historical Constructions

3D and spectral digital recording of cultural heritage monuments is a common activity for their documentation, preservation, conservation management, and reconstruction. Recent developments in 3D and spectral technologies have provided enough flexibility in selecting one technology over another, depending on the data content and quality demands of the data application. Each technology has its own pros/cons, suited perfectly to some situations and not to others. They are mostly unknown to humanities experts, besides having a limited understanding of the data requirements demanded by the research question. These are often left to technical experts who again have a limited understanding of cultural heritage requirements. A common point of view has to be achieved through interdisciplinary discussions. Such agreements need to be documented for their future references and re-uses. We present a method based on semantic concepts that not only documents the semantic essence of such discussions, but also uses it to infer a guidance mechanism that recommends technologies/technical process to generate the required data based on individual needs. Experts' knowledge is represented explicitly through a knowledge representation that allows machines to manage and infer recommendations. First, descriptive semantics guide end users to select the optimal technology/technologies for recording data. Second, structured knowledge controls the processing chain extracting and classifying objects contained in the acquired data. Circumstantial situations during object recording and the behaviour of the technologies in that situation are taken into account. We will explain the approach as such and give results from tests at a CH object.

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Identification and classification of objects in 3D point clouds based on a semantic concept

2019

PDF / RTF

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Analyse von Bildresiduen mit Machine-Learning im Rahmen von Kamera-Kalibrierungen

2017

RTF

Photogrammetrie ermöglicht es, Objekte mithilfe von Digitalbildern zu vermessen. Bei optimalen Messbedingungen sind Qualitätsunterschiede der abgeleiteten Maße vor allem auf die mathematische Modellierung des verwendeten Sensors und des Objektivs zurückzuführen. Photogrammetrische Kalibrierungen erfolgen meist mittels Bündelblockausgleichung. Diese gestattet es, vielerlei statistische Kennzahlen abzuleiten. Eine tiefer gehende Analyse der berechneten Parameter, Standardabweichungen, Korrelationen und deren Verteilungen kann Aufschluss darüber geben, ob das verwendete Kalibriermodell Schwächen aufweist. Solche Defizite können sich durch systematische Restfehler im Bild- oder Objektraum äußern. Da solche Restfehler auch zu Ungenauigkeiten in den daraus abgeleiteten Informationen führen können, ist deren mathematischer Nachweis und anschließende Kompensation zur Erzielung höchster Genauigkeiten unausweichlich. Neueste Ansätze nutzen Korrekturterme, um Residuensystematiken schon während der Bündelblockausgleichung zu modellieren. Dieser Beitrag beschreibt, wie auch Machine-Learning-
Techniken dabei helfen können, verbliebene systematische Abweichungen in Bildresiduen nachzuweisen, ohne dass hierzu ein Eingriff in die Bündelblockausgleichung notwendig ist. Dies wird im ersten Schritt anhand von Beispieldaten erläutert. Im zweiten Schritt wird die Wirkung dieser Vorgehensweise an einer realen Kamerakalibrierung verdeutlicht. Abschließend erfolgt eine Diskussion der im Zuge dieser Arbeit erzielten Resultate und möglicher Eignung dieses Verfahrens in der Praxis.

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COSCH - Vier Jahre interdisziplinärer Dialog zum Nutzen des kulturellen Erbes.

2017

RTF

KulturBetrieb - Magazin für innovative und wirtschaftliche Lösungen in Museen, Bibliotheken und Archiven

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Ontology-based Knowledge Representation for Recommendation of Optimal Recording Strategies - Photogrammetry and Laser Scanning as Examples.

2017

RTF

gis.Science

Experts’ knowledge about optical technologies for spatial and spectral recording is logically structured and stored in an ontology-based knowledge representation with the aim to provide objective recommendations for recording strategies. Besides operational functionalities and technical parameters such as measurement principles, instruments, and setups further factors such as the targeted application, data, physical characteristics of the object, and external influences are considered creating a holistic view on spectral and spatial recording strategies. Through this approach impacting factors on the technologies and generated data are identified. Semantic technologies allow to flexibly store this knowledge in a hierarchical class structure with dependencies, interrelations and description logic statements. Through an inference system the knowledge can be retrieved adapted to individual needs.

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