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

• URL: http://arxiv.org/abs/2507.16214v2
• Date: Thu, 24 Jul 2025 04:02:42 GMT
• Title: Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers
• Authors: Batu Candan, Simone Servadio,
• Abstract summary: This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering.<n>A Convolutional Neural Network (CNN), enhanced with image preprocessing, detects structural markers from chaser imagery.<n>A UKF framework is selected for its ability to handle nonlinear relative dynamics to estimate the full relative pose.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• AFD-STA: Adaptive Filtering Denoising with Spatiotemporal Attention for Chaotic System Prediction [4.833734041528231]
  AFD-STA Net presents a framework for predicting high-dimensional chaotic systems governed by partial differential equations.<n>The framework shows promising potential for realworld applications requiring simultaneous handling of measurement uncertainties and high-dimensional nonlinear dynamics.
  arXiv  Detail & Related papers  (2025-05-23T16:39:07Z)
• RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task [20.16344973940904]
  High-resolution remote sensing analysis faces challenges due to scene complexity and scale diversity.<n>We propose RSRWKV, featuring a novel 2D-WKV scanning mechanism that bridges sequential processing and 2D spatial reasoning.
  arXiv  Detail & Related papers  (2025-03-26T10:03:46Z)
• DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation [10.127634263641877]
  Adapting machine learning models to new domains without labeled data is a critical challenge in applications like medical imaging, autonomous driving, and remote sensing.<n>This task, known as Source-Free Unsupervised Domain Adaptation (SFUDA), involves adapting a pre-trained model to a target domain using only unlabeled target data.<n>Existing SFUDA methods often rely on single-model architectures, struggling with uncertainty and variability in the target domain.<n>We propose DRIVE, a novel SFUDA framework leveraging a dual-model architecture. The two models, with identical weights, work in parallel to capture diverse target domain characteristics.
  arXiv  Detail & Related papers  (2024-11-24T20:35:04Z)
• ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction [89.89610257714006]
  Existing methods prioritize higher accuracy to cater to the demands of these tasks. We introduce a series of targeted improvements for 3D semantic occupancy prediction and flow estimation. Our purelytemporalal architecture framework, named ALOcc, achieves an optimal tradeoff between speed and accuracy.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction [25.688521281119037]
  Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is challenging and crucial for optimizing downstream tasks. Traditional prediction approaches focus on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. We propose a novel context-conditionedtemporal predictive learning method to capture dependencies within 4D CSI data.
  arXiv  Detail & Related papers  (2024-09-16T04:15:36Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
  We present a method named iComMa to address the 6D camera pose estimation problem in computer vision. We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
  arXiv  Detail & Related papers  (2023-12-14T15:31:33Z)
• Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching [77.133400999703]
  Correlation based stereo matching has achieved outstanding performance. Current methods with a fixed model do not work uniformly well across various datasets. This paper proposes a new perspective to dynamically calculate correlation for robust stereo matching.
  arXiv  Detail & Related papers  (2023-07-26T09:47:37Z)
• Variational Voxel Pseudo Image Tracking [127.46919555100543]
  Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving. We propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking.
  arXiv  Detail & Related papers  (2023-02-12T13:34:50Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.