This paper estimates the pose of a noncooperative space target utilizing a direct method of monocular visual simultaneous location and mapping (SLAM). A Large Scale Direct SLAM (LSD-SLAM) algorithm for pose estimation based on photometric residual of pixel intensities is provided to overcome the limitation of existing feature-based on-orbit pose estimation methods. Firstly, new sequence images of the on-orbit target are continuously inputted, and the pose of each current frame is calculated according to minimizing the photometric residual of pixel intensities. Secondly, frames are distinguished as keyframes or normal frames according to the pose relationship, and these frames are used to optimize the local map points. After that, the optimized local map points are added to the back-end map. Finally, the poses of keyframes are further enumerated and optimized in the back-end thread based on the map points and the photometric residual between the keyframes. Numerical simulations and experiments are carried out to prove the validity of the proposed algorithm, and the results elucidate the effectiveness of the algorithm in estimating the pose of the noncooperative target.
Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm