Real-Time Monocular 3-D Pose Tracking for Noncooperative Spacecraft in Close Range

Real-time pose tracking for noncooperative spacecraft is crucial for numerous space missions, such as debris removal and docking. Existing pose tracking methods either suffer from shadow occlusion and cluttered backgrounds in space or lack computational efficiency. This study proposes a real-time system for tracking the six-degree-of-freedom (6-DOF) pose of a noncooperative spacecraft for proximity operations using a single calibrated camera. The proposed method parameterizes the pose in the Lie group of SE(3) and exploits the 3-D boundaries and 3-D contour, which are automatically extracted from the 3-D model of the spacecraft as geometric features (GFs). At each frame of the tracking process, the initial pose is predicted using an error state Kalman filter (ESKF). Using the initial pose, the tracking system searches the input image for the 2-D edge points that are associated with the 3-D GFs based on a conditional random field (CRF) and then rapidly determines the spacecraft pose in SE(3) by minimizing the distances of the projected 3-D GFs to their corresponding 2-D edge points using the M-estimation and Newton's method. The covariance of the optimized pose is estimated based on the Karush-Kuhn-Tucker (KKT) condition for pose optimization. With the estimated pose covariance, the ESKF is developed based on second-order autoregression (AR) in SE(3) to produce the maximum a posteriori estimate of the pose and initialize the pose tracking at the next frame. A sufficient number of synthetic and real trials indicate that the proposed method outperforms the existing methods in terms of accuracy and efficiency. The experimental results reveal that the proposed method achieves a rotation angle error of approximately 0.5° and relative position error (RPE) of approximately 0.6% and that the average runtime of the proposed method is approximately 0.02 s per frame.