Convolutional Neural Networks for Spacecraft Pose Estimation

About this technology

Overview

The software trains and tests convolutional neural networks to determine spacecraft pose estimation. It also contains functionality to perform image preprocessing, data augmentation and tensorflow model compression.The software takes a unique approach that incorporates more image data than other approaches while retaining the original spatial dimensions. Other approaches will down sample images significantly which makes the neural network lose a lot of spatial information retarding robustness.The software is designed to estimate the pose of a spacecraft based on an image of that spacecraft. This includes the relative position and attitude of the target satellite with respect to a chasing satellite. Application of this technology aids function such as active debris removal and formation flying among other applications.