Markers Identification for Relative Pose Estimation of an Uncooperative Target

TL;DR

This paper presents a CNN-based method for detecting structural markers on space debris to enable autonomous relative pose estimation, improving safety and efficiency in space debris removal operations.

Contribution

It introduces a novel image processing and CNN approach for marker detection on space debris, enhancing autonomous pose estimation capabilities.

Findings

Promising detection accuracy with advanced pre-processing techniques

Potential for improved safety in space debris removal

Supports autonomous operations in space missions

Abstract

This paper introduces a novel method using chaser spacecraft image processing and Convolutional Neural Networks (CNNs) to detect structural markers on the European Space Agency's (ESA) Environmental Satellite (ENVISAT) for safe de-orbiting. Advanced image pre-processing techniques, including noise addition and blurring, are employed to improve marker detection accuracy and robustness. Initial results show promising potential for autonomous space debris removal, supporting proactive strategies for space sustainability. The effectiveness of our approach suggests that our estimation method could significantly enhance the safety and efficiency of debris removal operations by implementing more robust and autonomous systems in actual space missions.