Projects | TLS-based Multi-Sensor-Systems
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AutoMap - Development of a robust positioning system for autonomous vehicles based on captured environmental information and GNSS/IMU dataDetermining the exact position of vehicles is not only crucial for autonomous driving, but also for many other applications. However, existing technologies, such as global navigation satellite systems (GNSS) or inertial measurement units (IMU), are reaching their limits due to interference and inaccuracies, especially in urban areas.Led by: Hamza Alkhatib, Sören VogelTeam:Year: 2023Funding: mFUND project | funded by the BMDV (Bundesministerium für Digitales und Verkehr)Duration: 2023-2025
© 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
Projects | Expert-based data analysis and quality processes
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AutoMap - Development of a robust positioning system for autonomous vehicles based on captured environmental information and GNSS/IMU dataDetermining the exact position of vehicles is not only crucial for autonomous driving, but also for many other applications. However, existing technologies, such as global navigation satellite systems (GNSS) or inertial measurement units (IMU), are reaching their limits due to interference and inaccuracies, especially in urban areas.Led by: Hamza Alkhatib, Sören VogelTeam:Year: 2023Funding: mFUND project | funded by the BMDV (Bundesministerium für Digitales und Verkehr)Duration: 2023-2025
© GIH
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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.sensDuration: 11/2022 - 11/2025
© 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.sensDuration: 11/2022 - 11/2025
Projects | Interdisciplinary Monitoring
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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