Successful space rendezvous missions rely upon accurate pose estimation of a target spacecraft. In this session, we will explore AI/machine learning workflows through hands-on and code-along exercises. You will gain insights into building a successful pose estimation algorithm using the new, state-of-the-art commercially available dataset known as Speed-UE-Cube. This workshop will cover the complete workflow from image pre-processing to deploying deep learning algorithms on hardware and was created in collaboration with Stanford University’s Space Rendezvous Laboratory (SLAB).

In this interactive hands-on workshop, you will: Familiarize yourself, write and run code entirely in the browser using MATLAB® Online™. Create and evaluate necessary components to succeed in AI modeling, by implementing an example of aircraft classification. Deep dive into an advanced, domain-specific application that showcases a complete workflow for accomplishing spacecraft pose estimation.

MathWorks® instructors and teaching assistants (TAs) will be available throughout the session to guide you. If the event is being held onsite, please bring your laptop, and install the Google Chrome™ browser beforehand.