I currently work as a Researcher on Simultaneous Localisation and Mapping for a small lunar rover. In the past I had professional experiences in Visual Effects Compositing for films, Virtual Reality and Computer Vision for UAV’s. While those branches seem very different, there is a lot of overlap when it comes to dealing with cameras and images, whether it’s in a manual or automated way. Check out my website and feel free to contact me if you want to know more: ludivigfoo@hotmailbla.com.

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Current and previous work:

ispace NASAEuropean Space Agency Double Negative

Publications:

2025: Absolute Localization through Vision Transformer Matching of Planetary Surface Perspective Imagery from a Digital Twin.
IEEE IROS 2025: Hangzhou
2023: Low-thrust rendezvous trajectory generation for multi-target active space debris removal using the RQ-Law
Advances in Space Research
2022: Figuring out where you are on the Moon: The selection and validation of different pose-estimation techniques for lunar surface robotics.
PhD Thesis / University of Luxembourg
2020: Building a piece of the moon: Construction of two indoor lunar analogue environments.
71st International Astronautical Congress (IAC)–The CyberSpace Edition
2020: A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles.
IEEE RA-L : ICRA 2020 : Paris
2019: Absolute Localization Through Orbital Maps and Surface Perspective Imagery: A Synthetic Lunar Dataset and Neural Network Approach
IEEE IROS 2019 : Macau
2015: Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers
Sensors 2015, 15(12), 31362-31391; doi: 10.3390/s151229861

Education:

PhD Computer Science University of Luxembourg, LUX
MSc Information and Computer Sciences University of Luxembourg, LUX
MA Digital Effects Bournemouth University, UK
BA Computer Visualisation and Animation Bournemouth University, UK
Product Design ISD Valenciennes, France

Online courses:

Nvidia DLI: Fundamentals of Accelerated Computing with CUDA C/C++
Nvidia DLI: Fundamentals of Deep Learning for Computer Vision
Nvidia DLI: Fundamentals of Deep Learning for Multiple Data Types
Stanford University: Machine Learning
MITx 6.00x: Introduction to Computer Science and Programming
BerkleyX CS-169.1x: Software as a Service