MSc Robotics Research Topics
This page is for UT Robotics students who want to do their MSc research project with the Department of Earth Observation Science.
Robotics and Earth observation have a lot in common. After all, robots need to capture data on their environment, determine where the data have been captured, and interpret the data to decide on required actions. The same is needed for monitoring the Earth. Common research topics therefore include sensor calibration, sensor registration, sensor fusion, sensor system design, data quality analysis, 3D scene reconstruction, SLAM, 3D scene understanding, object tracking, and change detection. Many of these topics are nowadays based on deep learning. For data capture, we mostly use cameras and laser scanners, but also IMUs and GNSS. Data can come from open datasets, companies, or from our sensors, for example. Sensor platforms may range from ground and underwater vehicles, via UAVs and aeroplanes, to satellites. Industrial robotics where perception is needed typically includes construction or maintenance, or some natural (unorganized) elements such as in agriculture or forestry.
To find your MSc research topic, please browse through our list of topics presented below or contact one of our staff members to discuss potential research topics within their fields of expertise.
Ville Lehtola – SLAM, multi-sensor fusion and machine learning, robot perception and cognition, autonomous ships, indoor mapping
Francesco Nex – SLAM, UAVs, UAV swarm, edge computing, real-time, deep learning, autonomous exploration and mapping
George Vosselman – sensor calibration, sensor registration, 3D scene reconstruction
Farzaneh Dadrass Javan - Sensor integration (RGB, thermal, Polarized, Depth camera), Videogrammetry, data fusion, scene understanding, deep learning, logistics and dropout drones
Bashar Alsadik – Path planning, Positioning and error analysis, 3D scene reconstruction, Image/sensor orientation, and Mobile Mapping Systems
Sander Oude Elberink - 3D modelling of indoor and outdoor scenes, data acquisition and processing for Digital Twins, combining close- and midrange Earth Observation sensor data with map data, information extraction from 3D point clouds
Feel free also to propose a topic of your interest. These topics may relate to what companies are doing. If you cannot find who to ask, ask the person you think is the closest and ask them who could be the right supervisor.
Unmanned Aerial Vehicles (UAVs) are increasingly used within an Internet of Things (IoT) network where the
Object detection is a fundamental topic in computer vision. Recently, deep learning has become an effective method and achieved great success in object detection.
The estimation of depth from a single image have been a Computer Vision topic for decades.
Calibration of multi-sensor systems is a pivotal step in ensuring the accuracy and reliability of data derived from these sensors.
The problem of 3D spatial perception involves the real-time construction and maintenance of a comprehensive and actionable representation of the environment using sensor data and prior knowledge.
Current laser scanners can operate with pulse repetition rates of 2 MHz, i.e., the time between emitted pulses is 0.5 µs.
This project involves the development and implementation of an advanced situational awareness system for a robotic platform, specifically designed for autonomous exploration
Unmanned Aerial Vehicles (UAVs) are increasingly used within a Mobile Edge Computation (MEC) network where the UAV is made intelligent by
While Graph-SLAM techniques have significantly contributed to mobile mapping advancements in indoor and urban environments, there remains a need to improve the quality of data gen
Unmanned Aerial Vehicles (UAVs) are increasingly used by public and private organizations such as the Politie, Brandweer or Rijkswatersta
Unmanned Aerial Vehicles (UAVs) are powerful flexible monitoring devices, and their data is increasingly us
GNSS-denied localisation for outdoor robots without GPS
MSc thesis project at ITC-EOS in collaboration with Demcon
Simultaneous Localization and Mapping (SLAM) techniques, especially graph-SLAM techniques, can be used for mob
Simultaneous Localization and Mapping (SLAM) techniques are critical for mobile mapping in indoor and urban environments.
Simultaneous Localization and Mapping (SLAM) has been foundational for autonomous systems, with GraphSLAM being a prominent variant that optimizes mapping as a graph-based optimization
Utilities form the essential infrastructure of a contemporary society, fulfilling fundamental requirements for both residents and industries.
UAVs are increasingly used to create 2D and 3D maps to support monitoring and emergency scenarios.
Neural Radiance Field (NeRF) stands as a category of neural networks adept at represent and render realistic 3D scenes environments using a set of 2D images.
This project aims to explore using monocular cameras in the real-time construction of 3D scene graphs in outdoor applications.
Simultaneous Localization and Mapping (SLAM) techniques, especially Extended Kalman Filtering techniques, can
In the past couple of years, Neural Radiance Fields (NeRF) [1] and methods derived from it have become a popular choice for solving different tasks s
Unmanned Aerial Vehicles (UAVs) have found significant applications in road monitoring, offering a versatile and efficient solution for infrastructur
The segmentation of multi-line raw LiDAR scans is a challenging and crucial problem in 3D scene understanding.