A Dual-Mode Pose Estimation Framework Using Monocular Vision and ArUco Markers for close proximity operation with cooperative targets.
- Paper ID
93741
- author
- company
Indian Institute of Technology Kanpur; Defence Research and Development Organization (DRDO)
- country
India
- year
2025
- abstract
Accurate six degrees of freedom (6DOF) pose estimation covering both orientation and position is essential for cooperative satellite operations, including autonomous docking, on-orbit servicing (OOS), and active debris removal (ADR). For satellites not designed with serviceability in mind, monocular-based pose estimation is challenging due to the lack of easily identifiable features. Installing fiducial markers, like ArUco markers, on future space assets provides detectable and identifiable features, improving robustness, reliability, and the economic viability of OOS/ADR missions. This approach supports standardized and modular designs for flexible space systems. However, traditional vision-based methods often struggle under space-specific challenges such as extreme lighting contrasts, motion blur, and sensor noise. This study addresses these gaps by exploring the use of passive, high-contrast black-and-white (BW) markers like ArUco, which are optimized for the space environment. Our research evaluates the feasibility of ArUco markers as fiducial elements for close-range satellite rendezvous, using a monocular color camera to provide reliable 6DOF pose estimation at close chaser-target distances (10 meters up to contact). The proposed system operates in two modes: Acquisition, which relies solely on observed scene information, and Tracking, which utilizes prior pose data to enhance marker identification and efficiency. The pose determination process starts with an image processing algorithm that detects markers using segmentation and contour extraction to isolate high-contrast BW markers. Subsequently, an image-to-model matching step identifies the markers, establishing 2D-3D correspondences that feed into a PnP solver for pose estimation. Using analytical P4P solutions and non-linear least-squares optimization, the system dynamically adapts to operational conditions for accurate results. Preliminary evaluations demonstrate that our framework achieves competitive accuracy and stability, showcasing its potential for real-time, on-orbit applications. By integrating ArUco markers into the pose estimation process, this framework facilitates efficient and reliable satellite servicing and debris removal operations, advancing the goals of space sustainability and autonomous navigation in future space missions.