Space Situational Awareness Instrumentation

Project description: The general objective of this project is to develop computer vision solutions for Space Situational Awareness with a focus on spacecraft pose estimation using multiple modalities.   Starting date: 01/01/2020 Duration: 48 months Funding source: LMO Researchers: Dr. Vincent Gaudilliere, Mohamed Adel Mohamed Ali, Prof. Djamila Aouada (PI) 

DIOSSA: Deep Learning-based In-orbit Space Situational Awareness

Project description: LMO in partnership with SnT (University of Luxembourg) is carrying out the Development of In-Orbit Servicing Space Situational Awarenesst. The Space Situational Awareness (SSA) payload autonomously derives the 6 Degrees of Freedom (DoF) pose estimation of a target space resident object under any illumination condition and is part of the spacecraft Guidance, Navigation … Continued

MEET-A – Multi-modal Fusion of Electro-optical Sensors for Spacecraft Pose Estimation Towards Autonomous in- Orbit Operations

Project description: Satellites autonomously meeting in a rendezvous approach is the next biggest revolution in space. This starts by endowing satellites with the capability of accurately and robustly determining their relative pose without cooperating with other spacecrafts. Existing solutions are still not accurate enough to be deployed in space. To enhance these approaches and enable … Continued

ELITE: Enabling Learning and Inferring compact deep neural network Topologies on Edge devices

Project description: The primary goal of the project “ELITE: Enabling Learning and Inferring compact deep neural network Topologies on Edge devices” is to investigate new ways to build compact DNNs from scratch by 1) using efficient latent representations and their factors of variations and 2) exploiting NAS based techniques for minimal deep architectural design. The … Continued