Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers

TL;DR

This paper introduces an adaptive relative pose estimation system combining CNN-based feature detection with dual noise tuning UKF to improve robustness in challenging debris removal scenarios.

Contribution

It presents a novel integrated framework with dual adaptive UKF tuning strategies for measurement and process noise, enhancing robustness in dynamic and uncertain environments.

Findings

Improved pose estimation accuracy in high-fidelity simulations.

Enhanced robustness against measurement outages and dynamic uncertainties.

Demonstrated effectiveness in realistic ADR scenarios.

Abstract

Accurate and robust relative pose estimation is crucial for enabling challenging Active Debris Removal (ADR) missions targeting tumbling derelict satellites such as ESA's ENVISAT. This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering to address this challenge. A Convolutional Neural Network (CNN), enhanced with image preprocessing, detects structural markers (corners) from chaser imagery, whose 2D coordinates are converted to 3D measurements using camera modeling. These measurements are fused within an Unscented Kalman Filter (UKF) framework, selected for its ability to handle nonlinear relative dynamics, to estimate the full relative pose. Key contributions include the integrated system architecture and a dual adaptive strategy within the UKF: dynamic tuning of the measurement noise covariance compensates for varying CNN measurement uncertainty, while adaptive tuning of the process noise covariance, utilizing measurement residual analysis, accounts for unmodeled dynamics or maneuvers online. This dual adaptation enhances robustness against both measurement imperfections and dynamic model uncertainties. The performance of the proposed adaptive integrated system is evaluated through high-fidelity simulations using a realistic ENVISAT model, comparing estimates against ground truth under various conditions, including measurement outages. This comprehensive approach offers an enhanced solution for robust onboard relative navigation, significantly advancing the capabilities required for safe proximity operations during ADR missions.