SMARTPOSE: Development of a Sample-Efficient, Model-Agnostic, Robust, Two-Stage POSE Estimator for Unknown Satellites

Abstract

Accurate relative pose estimation is critical for autonomous close-proximity satellite operations, such as on-orbit servicing and debris removal. However, this task remains highly challenging for unknown, non-cooperative targets due to the unavailability of geometric priors, scarce training data, and the extreme variations in lighting and backgrounds inherent to the space environment. Existing approaches for pose estimation can be categorized into: 1. model-based methods, which achieve higher accuracy but assume access to 3D CAD models of target satellites; and 2. model-free methods, which can be used for unknown satellites but typically suffer from reduced accuracy and robustness. To bridge this gap, this paper introduces a sample-efficient, model-free pose estimation framework that achieves high accuracy and robustness for unknown satellites while demonstrating strong generalization under the uncertain conditions inherent to the space environment. The proposed method utilizes a novel appearance aware 3D reconstruction to generate satellite model from images accounting for different lighting conditions during the training. This model is then used to generate a large, diverse dataset to train a pose predictor network (stage 1). The predicted pose is refined using the 3D reconstruction by utilizing the appearance information of the target image along with differentiable rendering (stage 2). Evaluated across SPEED+ and URSO Soyuz datasets, our approach achieves state-of-the-art accuracy and proves highly robust to test-time domain shifts, notably reducing rotation error by 80% on the challenging URSO Soyuz dataset.