Leibniz Universität Hannover Faculty Faculty of Civil Engineering and Geodetic Science
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Geodetic Institute
 
Geodetic Institute Hanover Teaching Theses

Master's theses at the Geodetic Institute

Open master's theses

  • Integrating Measurement Uncertainty for Enhanced Reliability in Digital Bathymetric Models
    Nowadays mapping of underwater topography has been greatly facilitated by high-resolution systems such as multibeam echosounders (MBES). Nevertheless, the extensive data collected by these instruments are often contaminated with varying degrees of errors. This calls for caution in constructing Digital Bathymetric Models (DBMs), particularly for critical applications like waterway navigation. This master thesis aims to explore and enhance the quality of DBMs by incorporating measurement uncertainties systematically into the modeling process.
    Led by: PD Dr.-Ing. Hamza Alkhatib, Bahareh Mohammadivojdan, M. Sc.
    Year: 2025
    © GIH
  • Bayesian Deep Learning for Distribution Prediction of TLS Uncertainties
    In modern geodetic applications, Terrestrial Laser Scanning (TLS) is a key technology for the acquisition of detailed 3D information. While TLS has demonstrated remarkable capabilities, the need for robust uncertainty modelling is becoming increasingly apparent in critical applications such as deformation analysis. An accurate understanding and quantification of systematic deviations in TLS measurements is essential to ensure the reliability of 3D data. This research seeks to address this aspect by investigating the uncertainties within TLS data, in particular the systematic deviation in distance measurement.
    Led by: PD Dr.-Ing. Hamza Alkhatib, Jan Hartmann, M. Sc.
    Year: 2025
    © GIH
  • Advanced Particle Filter-Based V2V Collaborative Localisation in Urban Environments
    This master thesis aims to develop and integrate a reliable Vehicle-to-Vehicle (V2V) collaboration approach into an existing Particle Filter framework. By incorporating the uncertainty information of leading vehicles, the method seeks to accurately quantify the filtered ego-vehicle's pose. The primary objective is to minimize longitudinal deviations, particularly in feature-poor environments, where current methods experience significant pose disturbances. Ultimately, this module aims to enhance localization robustness and accuracy in challenging urban scenarios.
    Led by: PD Dr.-Ing. Hamza Alkhatib, Marvin Scherff, M. Sc.
    Year: 2025
    © GIH
  • Advanced Particle Filtering for Quadruped Robot Navigation
    The aim of this master thesis is to adapt and evaluate an existing particle filter framework for quadruped robots. Originally developed for autonomous cars, the framework must be assessed and potentially modified to account for the differences in sensor configurations and the distinct characteristics of quadruped robots. Necessary improvements should be identified and implemented as needed.
    Led by: Dr.-Ing. Rozhin Moftizadeh, Christian Hartberger, M. Sc.
    Year: 2025
    © GIH
  • Real World Operational State Estimator for Autonomous Vehicles Based on LoD2 Maps
    The objective of this Master’s thesis is to incorporate real sensor data to further develop an existing simulation-based state estimation scheme. The main aims are: Investigate the limitations of the previously developed algorithm when paired with real data, Develop pre-processing algorithms that alleviate these limitations, Suggest necessary additions or modifications to the existing Multi Sensor System (MSS), and Assess the real-time capabilities of the newly developed scheme.
    Led by: PD Dr.-Ing Hamza Alkhatib, Mohamad Wahbah, M.Sc
    Year: 2025
    © GIH
  • Spatio-temporal Change Point detection in InSAR Persistent Scatterer Time Series
    The goal of this master's thesis is to utilize deep learning models to detect significant changes in trends and steps of PS time series in a spatio-temporal manner. This approach could help identify areas exhibiting significant land deformation in Germany.
    Led by: Dr.-Ing Mohammad Omidalizarandi, Kourosh Shahryarinia , M. Sc.
    Year: 2025
    © GIH
  • Robust Recursive State Estimation: Application in UAV Pose Determination
    This master's thesis initially focuses on investigating and studying various recursive parameter estimators that integrate blunder detection and mitigate their influence. Secondly, it focuses on decision-making and integrating a suitable outlier detector to specific recursive estimators that use fused sensor data to determine the pose of a UAV.
    Led by: Arman Khami, M. Sc.
    Year: 2025

Last Change: 20.01.25 Print

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