[2507.16214] Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers

Computer Science > Robotics arXiv:2507.16214 (cs) [Submitted on 22 Jul 2025 (v1), last revised 19 Mar 2026 (this version, v3)] Title:Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers Authors:Batu Candan, Murat Berke Oktay, Simone Servadio View a PDF of the paper titled Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers, by Batu Candan and 1 other authors View PDF HTML (experimental) 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 resi...