You can read more about our CanSat mission, which involves developing a miniature satellite housed within the dimensions of a standard soda can, by checking in for regular project updates here.

The adventure continues high above as we delve deeper into our CanSat project. While our primary mission captures atmospheric data, our secondary mission involves exploring the intricacies of depth estimation using stereo cameras and the power of machine learning. At the heart of this endeavor is a machine-learning model, specifically a convolutional neural network (CNN) that brings a semblance of depth perception to a machine. Let's explore how this ML model works and how it's the bedrock of our CanSat's secondary mission.

Welcome back to another blog post where we get to the nuts and bolts of our CanSat project by exploring the design choices, material selection and 3D printing process involved in bringing our CanSat to life.

Our team recently participated in the CanSat Nationals at the National STEM Learning Centre at York University, where we had the opportunity to finally launch our can. The core objective of our CanSat was to gather stereo images throughout its descent, using these captures to infer depth estimation through a tailor-made machine learning algorithm, developed and trained for this very task.