Predicting uncertainty in vision-based satellite pose estimation using deep evidential regression

Abstract

In this work, we demonstrate the use of evidential regression to quantify uncertainty in satellite pose estimation within vision-based deep-learning models trained on images of the Tango satellite. Our approach augments existing deep-learning models with uncertainty prediction capability using evidential regression. We show that the resulting uncertainty is well correlated with prediction errors, while maintaining comparable pointwise-prediction accuracies to existing models. Leveraging evidential regression to predict epistemic and aleatoric uncertainties, we demonstrate a median 0.996 correlation between network prediction error and these uncertainty estimates on keypoint coordinates within synthetic images. Additionally, we demonstrate that predicting on out-of-distribution images as well as increasing image noise directly amplifies uncertainty in pose estimation. By validating our framework's ability to quantify uncertainty, we enable robust decision-making in risky satellite operations.