Leibniz Universität Hannover Faculty Faculty of Civil Engineering and Geodetic Science
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Institute of Cartography and Geoinformatics
 
Institute of Cartography and Geoinformatics Institute Search for Persons Sester
Theses

Sester - Theses

Master Theses (finished)

  • Hololens 2 - Analysis of capabilities and quality
    The Hololens is a device, which captures information of the environment and creates a 3D model of it. At the same time, it is able to place virtual objects into the environment and thus allows AR-applications. The goal of the thesis is to investigate the potential of the Hololens for capturing indoor environments. This includes the acquisition of 3D point clouds and a thorough quality assessment. Subsequently, the point could has to be processed in order to segment important objects or features (e.g. walls, furniture). To this end, the use of Deep Learning models has to be considered.
    Led by: Kamalasanan, Sester
    Year: 2023
  • Future trajectory and Motion guidance with Augmented reality
    Controlling pedestrian motion pattern using augmented reality would require explainable visualizations to convince the user to change directions and speed of motion. Such AR visualizations should avoid cognitive overload and should provide motion guidance that are accurate representations of expected user actions to avoid conflicts / collisions. The focus of this master thesis would be to design and evaluate 3D motion guidance augmentations using AR emphasizing how such visualizations can avoid collisions between pedestrian / smartphone zombie. The student is expected to design and validate motion guidance visualizations in augmented reality
    Led by: Kamalasanan, Sester
    Year: 2023
  • Identification and analysis of movement patterns in trajectories
    In this work, movement patterns in trajectory datasets are identified with respect to the respective visited locations of a trajectory. For this purpose, further semantic information is assigned to the whereabouts points depending on the position, time of day, and duration of stay; the assignment of semantic information with respect to position is done using OpenStreetMap data. Another focus was on the identification of related trajectory segments, since the given dataset was anonymized as a consequence of data protection; for this purpose, coordinate prediction was performed for all trajectory endpoints in order to identify a suitable continuing starting point of another trajectory using a proximity search and temporal proximity. Recurrent motion pattern detection performed based on the whereabouts points does not produce meaningful patterns detected in multiple trajectories throughout the dataset for the datasets used; however, meaningful recurrent patterns are found for individual trajectories. An increasing level of detail in assigning categories with respect to whereabouts results in fewer recurring patterns, which, on the other hand, allow for greater meaningfulness given the interpretation of an observed person’s movement behavior.
    Led by: Golze, Feuerhake, Wage, Sester
    Team: Friderike Fischer
    Year: 2022
  • Development of a modular sensor platform for mobile detection of vehicle encounters
    Riding a bike in a shared traffic area with motor vehicles causes discomfort for many bicyclists. Avoiding busy roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles on most roads. Acquiring a dataset that collects smartphone sensor data on vehicle encounters could become the basis for a smartphone-based vehicle detector. Magnetometer and barometer readings are used as indicators of passing vehicles. In this thesis, a sensor platform is first constructed to collect smartphone and other sensor data while driving. The system is designed to be used with other sensor configurations in the future. A methodology is then presented to create a dataset of vehicle encounters based on data from a camera and a distance sensor on the sensor platform. This data set contains all important sensor data of a commercially available smartphone including the timestamp of vehicle encounters. Finally, a three-class classifier is trained and evaluated based on the data set. It is investigated which approach can provide a generalizable classifier. Approaches based on Random Forests are investigated for the classifier. The structure and parameters of a sliding window function are adjusted for feature generation.
    Led by: Wage, Feuerhake, Golze, Sester
    Team: Tim Schimansky
    Year: 2022
  • Hololens 2 – Evaluating 3D Mapping and Technical Capabilities
    In this study, the technical and 3D mapping capabilities of Hololens 2 was evaluated. The Microsoft Hololens 2 is a head-worn mobile mixed reality device that is capable of mapping its direct environment in real time. It is equipped with different sensors including four visible light tracking cameras and a depth sensor. The 3D map created using these sensor streams can be accessed by research mode. This makes Hololens 2 a powerful tool for mapping an indoor space. In this work, we evaluate the capabilities of Hololens 2 with respect to the task of the 3D indoor mapping, semantic segmentation and 3D modelling as the quality of scanned data highly influences the accuracy of reconstruction and segmentation.
    Led by: Vinu Kamalasanan, Monika Sester
    Team: Vishal Rudani
    Year: 2022
  • Trajectory anomaly detection using spectral clustering and RNN-based auto-encoder
    Anomaly detection is important, because anomalous behavior may indicate critical events or objects within diverse research areas and application domains. One of such domains is transport, especially integrated urban mobility. Trajectories of moving objects are good representations of their behaviors in surveillance data and useful in detecting anomalous behavior. On one hand, trajectories can provide more agent-based, long-term information comparing with simple physical features. On the other hand, comparing with raw video data, which is usually represented as a sequence of images, trajectory data requires less storage space and computational resources. Moreover, it has a wide variety of sources, such as GPS instruments and laser-scanners.
    Led by: Sester, Koetsier
    Team: Yao Li
    Year: 2020
  • Development of environmentally-balaced and congestion-avoiding routing algorithms by means of traffic simulation
    Due to the constantly growing volume of traffic in urban environments and the resulting problems such as increased air pollution, environmentally oriented approaches to achieve better urban sustainability of transport play an increasingly important role. This thesis deals with the development of environmentally-friendly routing algorithms and their validation in traffic simulations. The routing algorithm used is the A* - algorithm using the developed criteria as weights.
    Led by: Sester, Fuest
    Team: Christian Hartberger
    Year: 2019

Open Bachelor Theses

  • Exploring Herrenhausen Gardens
    Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience
    Led by: Feuerhake, Sester
    Year: 2023
  • Detection of Signatures in old Maps using Deep Learning
    Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.
    Led by: Thiemann, Sester
    Year: 2023
  • Bestimmung von Mustern in Fahrzeugtrajektorien
    Die Bewegungstrajektorien von Fahrzeugen erlauben Rückschlüsse auf raum-zeitliche Situationen. So können beispielsweise Haltepunkte detektiert werden oder auch Stausituationen, oder auch Anomalien wie temporär nicht zu befahrende Straßensegmente. In der Arbeit sollen in einem großen Trajektoriendatenbestand solche Muster automatisch erkannt werden. Der Datenbestand umfasst sehr viele Trajektorien. Bei Interesse kann ein Schwerpunkt auf die skalierbare Datenanalyse mittels Hadoop und Spark gelegt werden. Je nach Schwerpunkt ist die Arbeit sowohl als Bachelor- als auch als Masterarbeit bearbeitbar.
    Team: Feuerhake, Sester
    Year: 2020

Open Master Theses

  • Exploring Herrenhausen Gardens
    Development of an Location Based Interactive Mobile Web Application for Enriching Visitors' Knowledge and Experience
    Led by: Feuerhake, Sester
    Year: 2023
  • Detection of Signatures in old Maps using Deep Learning
    Old maps contain a lot of interesting information of the past reality. Most of maps are, however, only available in analogue form, and thus difficult to query and analyse automatically. The goal of this thesis is to explore modern deep learning methods to automatically detect signatures on old maps. There will be a concentration on certain types of objects, e.g. trees or buildings.
    Led by: Thiemann, Sester
    Year: 2023
  • Development of an approach for integrating various format data into a 3D voxel-based Urban Digital twin
    The advancements in instruments and methodologies for collecting, transmitting, analyzing, and representing three-dimensional (3D) geodata over the past few decades have opened up extensive possibilities for various applications. 3D geoinformation plays a pivotal role in the operational frameworks of Smart City technology that can be represented within an Urban Digital Twin concept. This involves utilizing diverse data from numerous sensors and designing an adaptive digital model that learns from and evolves alongside the real city.
    Led by: Shkedova, Feuerhake, Sester
    Year: 2023
  • Localization of mobile objects in the Absence of GPS/GNSS: A Hybrid 2D-3D Approach
    In today's dynamic landscape of autonomous vehicles and robotics, accurate and real-time localization is imperative. While 3D methods have been employed for vehicle localization, their time-consuming nature poses challenges. This research seeks to a novel hybrid approach, bridging the efficiency of 2D methods with the precision of 3D refinement, to offer a faster and more robust solution for vehicle localization.
    Led by: Mortazavi, Sester
    Year: 2023
  • Bestimmung von Mustern in Fahrzeugtrajektorien
    Die Bewegungstrajektorien von Fahrzeugen erlauben Rückschlüsse auf raum-zeitliche Situationen. So können beispielsweise Haltepunkte detektiert werden oder auch Stausituationen, oder auch Anomalien wie temporär nicht zu befahrende Straßensegmente. In der Arbeit sollen in einem großen Trajektoriendatenbestand solche Muster automatisch erkannt werden. Der Datenbestand umfasst sehr viele Trajektorien. Bei Interesse kann ein Schwerpunkt auf die skalierbare Datenanalyse mittels Hadoop und Spark gelegt werden. Je nach Schwerpunkt ist die Arbeit sowohl als Bachelor- als auch als Masterarbeit bearbeitbar.
    Team: Feuerhake, Sester
    Year: 2020

Last Change: 10.03.23 Print

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