Spacecraft Pose Estimation

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Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter
Oct 5, 2026Pol Francesch Huc, Simone D'AmicoKalman Filtering Relative Pose Estimation
Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305
Vision Foundation Models with Synthetic-Only Training for Monocular Spacecraft Pose Estimation
Sep 22, 2026John Church, Vazghen NikolianVision Foundation Model Adaptation Object Pose Estimation
Both authors contributed equally to this work; author order is alphabetical.
DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching
Jul 15, 2026Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi +5Relative Pose Estimation Spacecraft Rendezvous and Proximity Operations
Undergraduate Student, Wellesley College, Wellesley, MA 02481, USA · Undergraduate Student, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Research Scientist, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +5
GAP-GDRNet: Geometry-aware monocular 6D pose estimation for spacecraft using synthetic geometric supervision
Jul 2, 2026Zongwu Xie, Yonglong Zhang, Yifan Yang +2Object Pose Estimation Spacecraft Pose Estimation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
Precision-Aware Illumination-Disentangled Vision Transformer for Spacecraft 6D Pose Estimation
Jun 10, 2026Zongwu Xie, Yifan Yang, Yonglong Zhang +3Vision Transformer Object Pose Estimation
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation
May 29, 2026Abid Ali, Arunkumar Rathinam, Djamila AouadaVision Foundation Model Adaptation Object Pose Estimation
CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
May 19, 2026Antoine Legrand, Renaud Detry, Christophe De VleeschouwerSynthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Department of Electrical Engineering (ELEN), ICTEAM, UCLouvain · Department of Electrical Engineering (ESAT), KU Leuven · Aerospacelab +1
Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction
May 19, 2026Zongwu Xie, Yonglong Zhang, Yifan Yang +2Synthetic-to-Real Domain Adaptation Synthetic Data Generation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth
May 9, 2026Yongliang Zhen, Bo LÜ, Hang Yang +1Object Pose Estimation Stereo Matching
School of Physics, Northeast Normal University, Changchun 130024, China · Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun 130033, China

Oct 5, 2026, cs.ROJ/K move · Enter open · S save

Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter

Pol Francesch Huc, Simone D'Amico

Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305

Kalman Filtering Relative Pose Estimation Spacecraft Rendezvous and Proximity Operations Visual Odometry Spacecraft Pose Estimation

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This work presents a novel learning-based pipeline for pose estimation of unknown spacecraft using only monocular images from a single servicer. The approach combines a transformer-based neural network with a Multi-State Constraint Kalman Filter (MSCKF) to estimate the pose (i.e., position and orientation of the target spacecraft relative to the camera) throughout rendezvous and proximity operations. Unlike existing vision-based methods that require prior knowledge of the target shape or inertia properties, rely on additional sensing modalities such as depth, lidar, or stereo, or only recover translation up to scale, the proposed pipeline generalizes to previously unseen spacecraft using a single monocular camera. The transformer network estimates the odometry, the change in pose between images up to scale, from SuperPoint features matched by LightGlue. The MSCKF uses these pseudo-measurements along with an orbit and attitude kinematics model to estimate the pose of the target. In particular, the relative orbit elements, the target's attitude with respect to the servicer's camera, and the associated angular velocity are estimated directly by the filter. Given the monocular approach and short distance to the target, the full observability of the range to the target is recovered via attitude maneuvers by the servicer. The method is trained and evaluated on a re-rendered high-resolution version of the SPE3R dataset, which includes synthetic images of 103 spacecraft. Eleven of these spacecraft are held out during training to evaluate the generalization to unseen targets. Monte Carlo simulations are then used to evaluate the navigation pipeline on rendered trajectories of the held out spacecraft. The results demonstrate that learned vision pipelines as a front-end for Kalman filters provide median errors of 3.7° in attitude and 2.2% of range in ROE when navigating about unknown targets.

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