Aktuelle Projekte der AG Expertengestützte Datenanalyse und Qualitätsprozesse

  • Integrity contained navigation based on vehicle data and constrained collaborative information
    Multi-Sensor System (MSS) georeferencing is a challenging task in engineering which should be dealt with in the most accurate way possible. An example of a MSS is an autonomous car which drives through an environment and should be able to locate itself safely. The easiest and most straightforward way of georeferencing is to rely on the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data lack enough accuracy to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for such errors and to improve the results sufficiently. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information which can directly influence the outputs. Such information could be e.g. geometrical features of the environment in which the MSS runs through.
    Leaders: Ingo Neumann
    Team: Rozhin Moftizadeh
    Year: 2020
    Sponsors: DFG-Graduiertenkolleg i.c.sens
    Lifespan: seit 2020
  • Validation and quality assurance concepts for collaborative multi-sensor-systems
    The collaboration of several multi-sensor systems (MSS) has the potential to compensate individual shortcomings, e.g. by sharing navigational information among the nodes of a dynamic sensor network. Within the context of autonomous driving knowledge of the quality of the collected data and the derived trajectory is of great importance. Therefore, uncertainty modelling and propagation is a crucial issue - starting from the single sensors along the entire process chain (e.g. including the calibration and synchronization of the sensors within each MSS) up to the resulting products like 3D mapping information of the environment and the trajectory of the MSS. Numerous investigations deal with more or less specific aspects of the quality assurance of an MSS. Within this doctoral project the focus lies on the quality modelling of the MSS under investigation by the research training group (RTG). Building on this, the gain by the collaboration of MSS for the uncertainty can be evaluated in a subsequent project.
    Leaders: Ingo Neumann
    Team: Franziska Altemeier
    Year: 2019
  • 3D HydroMapper
    Im Rahmen des Verbundprojektes wird ein Messsystem zur Erfassung von Hafenbauwerken entwickelt. Ziel ist es, die Bausubstanz über und unter Wasser möglichst automatisiert, qualitätsgesichert und reproduzierbar mit einem hybriden Multi-Sensor-System zu erfassen. Die Bauwerksschäden sollen mittels Mustererkennungsmethoden automatisch erkannt und klassifiziert werden.
    Leaders: Ingo Neumann, Hamza Alkhatib
    Team: Frederic Hake
    Year: 2018
    Sponsors: Förderprogramm für Innovative Hafentechnologien (IHATEC) unterstützt durch das Bundesministerium für Verkehr und digitale Infrastruktur (BMVI)
    Lifespan: 12/2018 - 11/2021
  • Bayesian adaptive robust adjustment of multivariate geodetic measurement processes with data gaps and nonstationary colored noise
    This research field aims at the development of a unified robust adjustment theory and of corresponding computationally efficient expectation maximization (EM) algorithms to handle outliers, data gaps, colored noise and cross-correlations within geodetic measurement series simultaneously. Various kinds of stationary and nonstationary Gauss-Markov models are investigated (see the figure below). To include given prior information for the unknown parameters, Bayesian models and inferential techniques are also devised. Applications include the geo-referencing of a static multi-sensor system and deformation monitoring of an arch bridge.
    Leaders: Boris Kargoll, Hamza Alkhatib, Jens-André Paffenholz
    Team: Boris Kargoll, Hamza Alkhatib, Jens-André Paffenholz, Alexander Dorndorf, Mohammad Omidalizarandi
    Year: 2018
    Sponsors: DFG
    Lifespan: 2018-2021
  • Analysis of the correlation structure of TLS point clouds
    An improved stochastic model for TLS observations is of main importance, particularly when the observations are used in least-squares adjustment. Indeed, the best unbiased estimates of unknown parameters in linear models have the smallest expected meansquared errors as long as the residuals are weighted with their true variance covariance matrix.
    Team: Gaël Kermarrec, Hamza Alkhatib
    Year: 2018
    Lifespan: since 2018
  • Integre informationsbasierte Georeferenzierung
    Sowohl innerhalb geschlossener Räumlichkeiten mit komplexen räumlichen Strukturen (z.B. Bürogebäude) als auch in städtischen Umgebungen, mit einer Vielzahl an höheren Gebäuden, ist eine integre Georeferenzierung von kinematischen Multi-Sensor-Systemen (MSS) nur höchst aufwendig zu realisieren, da u.a. genaue und zuverlässige GNSS-Beobachtungen aufgrund von Abschattungen nicht zur Verfügung stehen. Echtzeitprozessierung oder hohe Genauigkeitsansprüche werden so nur sehr schwer erreicht.
    Leaders: Ingo Neumann
    Team: Sören Vogel
    Year: 2017
    Sponsors: DFG-Graduiertenkolleg i.c.sens
    Lifespan: seit 2017
  • Alternative Verfahren zur Modellierung von Unsicherheiten in ingenieurgeodätischen Prozessen
    Im Guide to the Expression of Uncertainty (GUM) wird eine Unterteilung der Unsicherheiten in zufällig und systematisch wirkende Einflüsse vorgeschlagen. Im Rahmen dieses Projekts sollen insbesondere die systematischen Unsicherheiten mit Hilfe von Fuzzy-, Bayes- und Monte Carlo-Verfahren ermittelt werden. In diesem Zusammenhang werden Laserscanning- und Wertermittlungsdaten untersucht.
    Leaders: Hamza Alkhatib, Ingo Neumann
    Team: Hamza Alkhatib
    Year: 2009
    Lifespan: seit 2009