Sensor AI Lab
AI for Sensor Networks
Sensors are everywhere – measuring, processing and inferring from the environment. We also carry sensors with us personally, wherever we go. These sensors are present in smartphones and activity trackers, and provide information about where we are, how we are moving and what we are doing. Technological advances have made sensors more available and more accurate over recent years, opening up many exciting applications.
The field of sensor fusion focuses on combining data from different types of sensors in order to extract more information than that available from each sensor alone. Physical knowledge can be used, for instance about how a system can move over time or about sensor properties. AI can also be used: new models can be established using data from sensors and sensor networks. Sensor AI unites the fields of sensor fusion and AI, bringing physical knowledge into AI to enable the extraction of more information from available sensor data.
The Sensor AI Lab focuses on developing novel algorithms, and on applying these tools in different fields. Examples include human motion estimation; distributed learning in sensor networks; and navigation of swarms of multiagent systems such as robots, ships, drones and satellites.
The Sensor AI Lab is part of the TU Delft AI Labs programme.
The Team
Directors
Manon Kok
Faculty of 3mE
Lab director
Raj Thilak Rajan
Faculty of EEMCS
Lab director
PhD students
Thomas Edridge
PhD student
Ruiyuan Li
PhD student
Zhonggang Li
PhD student
Kaiwen Zhang
PhD student
Associated researchers
Clara Menzen
Associated researcher
Ellen Riemens
Associated researcher
Frida Viset
Associated researcher
Associated faculty
Geert Leus
Associated faculty
Ajay Seth
Associated faculty
Alle-Jan van der Veen
Associated faculty
Jan-Willem van Wingerden
Associated faculty
Education
Courses
- Filtering and Identification (SC42025)
- Estimation and Detection (ET4386)
Master projects
Ongoing Master Projects
- Model-based sensor fusion to estimate kinematics of wheel-chair propulsion (Xiaohan Tang)
- Low rank approximations in Gaussian processes (Ban Hanyuan)
Finished Master Projects
- Scalable magnetic field modeling using structured kernel interpolation for Gaussian process regression, 2023 (Marnix Fetter).
- Magnetic field SLAM using an inertial human motion suit and reduced rank Gaussian process regression, 2022 (Thijs Veen).
- Bathymetry SLAM using reduced rank Gaussian Processes and DVL range measurements - For real-time underwater position estimation, 2022 (Danny Looman).
- Sensor fusion for estimating joint kinematics and kinetics of biomechanical systems - Validation using a robotic manipulator, 2021 (Jelle Boelens).
- Autonomous Landing of an Unmanned Aerial Vehicle, 2022 (Siddhy Ganesh Shetty)
- Cooperative Localization of Unmanned Aerial Vehicles using ADS-B, 2022 (Xuzhou Yang )
- Robust Formation Control against Observation Losses, 2022 (Zhonggang Li)
- Distributed Gaussian Process for Multi-agent Systems, 2022 (Peiyuan Zhai)
- Detect and Avoid for Autonomous Agents in Cluttered Environments, 2021 (Mosab Diab)
- Distributed Particle Filtering, 2022 (Rui Tang)
- Targetless Camera-LiDAR Calibration for Autonomous Systems, 2021 (Bichi Zhang)
- Time Synchronization for Anchorless Satellite Networks, 2021 (Felix Abel)
- Edge State Kalman Filtering for Distributed Formation Control Systems, 2021 (Martijn van der Marel)
- Energy-Efficient Particle Filter SLAM for Autonomous Exploration, 2021 (Elke Salzmann)
