Monocular Satellite Pose Estimation Based on Uncertainty Estimation and Self-Assessment

Estimating the pose (position and orientation) of a known but uncooperative satellite from a monocular image is a promising technique in space missions. Existing methods use deep neural networks and yield significant accuracy improvements over traditional ones. However, orbital applications require not only accuracy and real-time performance, but also the ability to know when and where the algorithm will fail. We develop a keypoint-set-based method to achieve a better balance between accuracy, efficiency, and reliability. To achieve real-time performance, we first adopt a lightweight neural network to predict the semantic keypoints in the monocular image and then estimate the pose by solving the perspective-n-point (PnP) problem. To address the drop in pose estimation accuracy caused by the significant reduction in parameters, we extend the basic keypoint-set-based approach via aleatoric uncertainty modeling, considering both spatial (coordinate regression) and semantic (index classification) uncertainties. Specifically, spatial uncertainty modeling involves predicting Gaussian distribution parameters for the coordinates of each element. We then incorporate a weight for each keypoint when estimating the pose by solving the PnP problem and significantly improve the pose estimation accuracy as a result. We also explore semantic uncertainty and add a self-assessment mechanism using the mean entropy of keypoint logits to identify incorrect pose estimates. Extensive experiments on the SPEED dataset highlight the flexibility and effectiveness of our method.