Theses – Institute of Cartography and Geoinformatics

Master Theses (finished)

Development of a cross-domain calibration for use on LiDAR and camera sensors on mobile mapping systems

Mobile mapping has been popularized over the last few years with special interest for re- search purposes. In this context, the Institute of Cartography and Geoinformatics (IKG) has developed a custom mobile mapping bike, equipped with two cameras and two Light Detection and Ranging (LiDAR) sensors, all connected to a Latte Panda Sigma com- puter. Due to the self-constructed setup, the geometric relationships between the sensors are initially unknown. Consequently, the aim of this thesis is to implement a workflow for determining the relative orientation between the sensors and to leverage the results for a visual validation through point cloud colorization using camera imagery. To establish the cross-sensor relations, orthogonal trihedrons are used as calibration tar- gets. The normal vectors from each planar surface are used as features to estimate the rotations alignment, where as the intersection point of all three planes is used to calculate the translation. For the plane extraction in the images, Augmented Reality University of Cordoba (ArUco) markers are used, where as Random Sample Consensus (RANSAC) is applied to the point cloud data. The core algorithm used to determine the relative orientation is the Kabsch algorithm, which is based on singular value decomposition. The initial calibration is further refined with a tracking-based approach, incorporating addi- tional measurements and model fitting to enhance accuracy and stability. Calibration results with a positional error of 2.052 cm in 2 m distance and an angular error of 0.0660° could be archived. After inspecting the colorized point cloud, a non-linear error propagation could be detected, suggesting that further improvements for the camera calibration are necessary. Moreover, different target patterns are advised to minimize their effect on the LiDAR measurements accuracy.

Led by: Brenner, Wage, Schimansky

Team: Florian von Loh

Year: 2025
Evaluation of SLAM algorithms for a bicycle LiDAR mobile mapping system

This thesis evaluates SLAM algorithms for a LiDAR-equipped bicycle mapping system. The goal is to adapt various SLAM packages to the system, assess their performance, and compare results with reference data. Tasks include calibrating the system, compensating for motion, and integrating navigation data. Resources include access to the mapping platform and datasets. Proficiency in Python and experience with point clouds are required. The research aims to benchmark SLAM performance for bicycle mapping.

Led by: Schimansky

Team: Siyuan Ren

Year: 2024
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

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

Schimansky - Theses

Master Theses (finished)

Open Bachelor Theses