Successful space rendezvous missions rely upon accurate pose estimation of a target spacecraft. In this session, we will explore AI/machine learning workflows in MATLAB® 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 is presented in collaboration with Stanford University’s Space Rendezvous Laboratory (SLAB).
In this hands-on workshop, you will write and run code entirely in the browser using MATLAB® Online™. You will learn how to apply principles of AI (machine learning, deep learning, domain-specific processing) to aircraft and spacecraft object detection workflows.
This interactive hands-on session will include the following:
- Familiarizing yourself with MATLAB® Online™ and AI tools.
- Creating and evaluating 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.
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