Satellite Pose Estimation Challenge: Dataset, Competition Design and Results

TL;DR

This paper presents a comprehensive analysis of a satellite pose estimation challenge, introducing a new dataset and evaluating various monocular vision-based methods to advance spaceborne computer vision capabilities.

Contribution

It introduces the first publicly available satellite imagery dataset and provides an in-depth analysis of competitive approaches and their challenges in satellite pose estimation.

Findings

Performance varies significantly across approaches.

Dataset characteristics influence estimation accuracy.

Key factors affecting challenge difficulty identified.

Abstract

Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular vision-based approaches and pushing the state-of-the-art on this problem. This work is based on the Satellite Pose Estimation Dataset, the first publicly available machine learning set of synthetic and real spacecraft imageries. The choice of dataset reflects one of the unique challenges associated with spaceborne computer vision tasks, namely the lack of spaceborne images to train and validate the developed algorithms. This work briefly reviews the basic properties and the collection process of the dataset which was made publicly available. The competition design, including the definition of performance metrics and the adopted testbed, is also discussed. The main contribution of this paper is the analysis of the submissions of the 48 competitors, which compares the performance of different approaches and uncovers what factors make the satellite pose estimation problem especially challenging.