SDPENet: A Lightweight Spacecraft Pose Estimation Network With Discrete Euler Angle Probability Distribution

Utilizing deep learning techniques for spacecraft pose estimation enables using low-cost sensors like monocular cameras. However, the existing methods have drawbacks, such as complex models or low estimation accuracy. Therefore, this letter proposes the Spacecraft Discrete Pose Estimation Network (SDPENet). Firstly, we design a feature fusion network and a pose estimation head applicable to the spacecraft pose estimation task and devise the Spatial-Semantic Interaction Attention (SSIA) mechanism for feature fusion. Secondly, the discrete Euler angle probability distribution is proposed to represent the spacecraft attitude, significantly reducing the number of parameters while improving the accuracy. Finally, we put forward three data augmentation methods named CropAndPad, DropBlockSafe and Z-axis Rotation Safe to improve the performance of the network for the spacecraft pose estimation task. The experimental results demonstrate that, compared with the existing works, the errors in the spacecraft position and attitude estimated by SDPENet are reduced by 8.7%–83.1% and 31.7%–87.8% respectively, and simultaneously, the number of parameters is decreased by 33.3%–82.4%.

I. Introduction

Currently, numerous spacecraft in space are undertaking different on-orbit missions, including in-orbit services for in-service spacecraft, such as rendezvous and docking, space refueling, etc [1]. Decommissioned spacecraft, malfunctioning spacecraft, and space debris need to be moved to graveyard orbits or made to re-enter the atmosphere to avoid threatening other space facilities and relieve the tense situation of orbital resources [2]. Because of the situations mentioned above, the demands for space missions such as On-Orbit Services (OOS) and Active Debris Removal (ADR) are steadily increasing. They are considered critical capabilities in the aerospace field in the next decade [3]. Numerous research projects have been carried out regarding these space missions, for example, the Phoenix Program of the Defense Advanced Research Projects Agency of the United States [4] and the ClearSpace-1 mission of the European Space Agency [5].