Schimansky - Theses

Master Theses (finished)

  • 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

Open Bachelor Theses

  • Unveiling the Wireless Jungle
    With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.
    Led by: Schimansky, Golze
    Year: 2023
  • Mobile Mapping Bike LiDAR Evaluation
    Das ikg setzt bereits seit einigen Jahren ein Auto basiertes LiDAR Mobile Mapping System zum Erfassen von Punktwolken ein. Diesen bieten vielfältige Analyse- und Visualisierungsmöglichkeiten. Allerdings ist die Nutzung des Messsystems auf mit dem Auto befahrbare Straßen beschränkt und die Prozessierung hängt von proprietärer Software ab. Um die Nutzungsmöglichkeiten zu erweitern wurde daher am ikg ein Lastenfahrrad basiertes Mobile Mapping System konstruiert. Dazu wurde ein Trike mit E-Unterstützung um ein Multisensorsystem erweitert, bestehend aus: LiDAR, RTK-GNSS, IMU und Erweiterungsmöglichkeiten um z.B. eine Thermalkamera. Dank der robusten Positionierung sind auch Messfahrten durch partiell abgeschattete Bereich (auch durch Innenbereiche) möglich. Auf dem Bordrechner werden die eingehenden Sensorstreams mittels ROS aufgezeichnet und bieten so individuelle Möglichkeiten zur weiteren Verarbeitung und Erweiterung des Systems. Ziel einer Abschlussarbeit wäre die Evaluation der resultierenden Punktwolke in Hinblick auf ihre relative Genauigkeit, sowie im Vergleich zum hochgenauen bisherigen System.
    Led by: Schimansky, Wage
    Year: 2023

Open Master Theses

  • Unveiling the Wireless Jungle
    With the increasing amount of wearables, electric cars and wide spread of WiFi home routers, the density and variety of wireless signals is increasing drastically. Different types of connections such as WiFi, Bluetooth, Bluetooth Low Energy and LTE is found in every European city. Even if they are not directly visible, it is possible to capture the emitted signals, e.g. via smartphone. Depending on the topic, there could be various analysis approaches with different goals for working with this type of data. However, the first step is always to collect additional data in order to become familiar with the process of collecting and exploring the data itself.
    Led by: Schimansky, Golze
    Year: 2023