Projects | TLS-based Multi-Sensor-Systems
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Deformation analysis based on terrestrial laser scanner measurements (TLS-Defo, FOR 5455): Uncertainty of the surface approximationGeodetic deformation analysis involves the statistical analysis of geometric changes in two or more states. To exploit the full potential of established surface-based measurement techniques, such as terrestrial laser scanning (TLS), continuous local and global modelling of the monitored surface is required. The project ‘Uncertainty of Surface Approximation’ focuses on the investigation of the interaction between measurement and model uncertainties in the context of surface model selection. These components are closely related, since the amount of model uncertainty is directly influenced by the interaction between the complexity of the measured object, such as roughness and sharp edges, and the spatial density of measurement points over the object. To address this, the project differentiates between three subtopics: TLS uncertainty budget, model uncertainty and the application of fractal geometry as a methodological tool to achieve the primary project goal.Led by: Ingo Neumann, Mohammad OmidalizarandiTeam:Year: 2023Funding: DFGDuration: 10/23 – 09/27
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Quality control of building components using quadruped robots in challeging enviromentsThe aim of this postdoctoral project, which was carried out as part of the DFG-funded Research Training Group i.c.sens, was to establish the methodological foundations for the autonomous navigation of a quadruped robot, as this is an essential prerequisite for robot-assisted quality inspection in construction environments.Led by: Prof. Dr.-Ing. Ingo Neumann, PD Dr.-Ing. Hamza AlkhatibTeam:Year: 2022Funding: DFG - GRK 2159 i.c.sens until November 2025Duration: 11/2022 - a.w.
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© GIH
Projects | Expert-based data analysis and quality processes
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Uncertainty Modeling for Kinematic LiDAR-based Multi-Sensor SystemsGoal of this PhD project is to investigate methods to enable a consistent estimation of uncertainties for LiDAR-based MSSs, while dealing with the challenges caused by the uncertainties of individual sensors and their interactions in the system.Led by: Prof. Dr.-Ing. Ingo NeumannTeam:Year: 2022Funding: DFG - GRK 2159 i.c.sens until November 2025Duration: 11/2022 - until further notice
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© GIH | Dominik Ernst
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Development of a collaborative robust Particle Filter for State Estimation with Stochastic and Quantity-based Uncertainties in Sensor NetworksPrecise vehicle localization is a critical requirement for autonomous driving, especially in urban settings where GNSS signals often fail. To address this challenge, an advanced Particle Filter framework estimates vehicle pose by fusing 3D LiDAR data with complementary sensor inputs. The primary motivation is to achieve low-decimetre localisation accuracy despite the complexities of urban environments.Led by: PD Dr.-Ing. Hamza AlkahtibTeam:Year: 2022Funding: DFG - GRK 2159 i.c.sens until November 2025Duration: 11/2022 - until further notice
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Projects | Interdisciplinary Monitoring
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Large-Scale InSAR Deformation Monitoring Using Realistic Simulation-Based Training of a Deep Learning ModelLarge-scale land surface deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) requires robust detection of changes in long-term deformation trends. However, accurate change point (CP) detection remains challenging due to the complex characteristics of InSAR time series, including seasonal and quasi-periodic components, as well as noise. Classical statistical methods and many existing deep learning approaches rely on restrictive assumptions or training data that do not fully represent real-world InSAR time series, resulting in limited generalization capability and scalability for large-scale operational applications. This study focuses on the use of deep learning models to address these challenges.Led by: Prof. Dr.-Ing. Ingo Neumann, Dr.-Ing. Mohammad OmidalizarandiTeam:Year: 2024Funding: DAAD Research GrantDuration: 10/2024 - 09/2027
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© GIH
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Deformation analysis based on terrestrial laser scanner measurements (TLS-Defo, FOR 5455): Uncertainty of the surface approximationGeodetic deformation analysis involves the statistical analysis of geometric changes in two or more states. To exploit the full potential of established surface-based measurement techniques, such as terrestrial laser scanning (TLS), continuous local and global modelling of the monitored surface is required. The project ‘Uncertainty of Surface Approximation’ focuses on the investigation of the interaction between measurement and model uncertainties in the context of surface model selection. These components are closely related, since the amount of model uncertainty is directly influenced by the interaction between the complexity of the measured object, such as roughness and sharp edges, and the spatial density of measurement points over the object. To address this, the project differentiates between three subtopics: TLS uncertainty budget, model uncertainty and the application of fractal geometry as a methodological tool to achieve the primary project goal.Led by: Ingo Neumann, Mohammad OmidalizarandiTeam:Year: 2023Funding: DFGDuration: 10/23 – 09/27
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