Lightweight Monocular Relative Pose Estimation of Spacecraft via Scale-Adaptive Multi-Task Learning

Abstract

Accurate monocular pose estimation of spacecraft is essential for on-orbit servicing and proximity operations, yet this remains challenging because of large variations in the target scale and limited onboard computational resources. To address these challenges, this paper proposes SAPose, a lightweight geometry-guided pose-estimation framework that integrates multi-task keypoint prediction with geometric pose recovery. The proposed network jointly performs spacecraft detection and semantic keypoint localization using a stage-aware lightweight backbone and a scale-adaptive feature fusion structure, thereby enhancing geometric feature representation across different target scales. For distant spacecraft occupying only a small portion of the image, an adaptive coarse-to-fine inference strategy selectively activates ROI-based secondary refinement to recover weakened structural details while avoiding unnecessary computation for medium- and large-scale targets. In addition, confidence-ranked keypoint selection and object-space nonlinear refinement are employed to improve the stability and accuracy of pose recovery. Experiments on the Spacecraft Pose Estimation Dataset (SPEED) and the Spacecraft Keypoint Dataset (SKD) demonstrate that SAPose achieves competitive pose estimation accuracy with a compact model size and efficient inference, providing a practical balance between accuracy and computational cost for monocular spacecraft pose estimation.