High-accuracy real-time satellite pose estimation for in-orbit applications

Vision-based relative pose estimation plays a pivotal role in various space missions. Deep learning enhances monocular spacecraft pose estimation, but high computational demands necessitate model simplification for onboard systems. In this paper, we aim to achieve an optimal balance between accuracy and computational efficiency. We present a Perspective-n-Point (PnP) based method for spacecraft pose estimation, leveraging lightweight neural networks to localize semantic keypoints and reduce computational load. Since the accuracy of keypoint localization is closely related to the heatmap resolution, we devise an efficient upsampling module to increase the resolution of heatmaps with minimal overhead. Furthermore, the heatmaps predicted by the lightweight models tend to show high-level noise. To tackle this issue, we propose a weighting strategy by analyzing the statistical characteristics of predicted semantic keypoints and substantially improve the pose estimation accuracy. The experiments carried out on the SPEED dataset underscore the prospect of our method in engineering applications. We dramatically reduce the model parameters to 0.7M, merely 2.5% of that required by the top-performing method, and achieve lower pose estimation error and better real-time performance.