Hybrid iteration and optimization-based three-dimensional reconstruction for space non-cooperative targets with monocular vision and sparse lidar fusion

The measurement of reference pose and geometric size for space non-cooperative targets is an essential premise of on-orbit servicing. In this paper, a three-dimensional (3-D) reconstruction and autonomous geometric parameter identification method is proposed. An autonomous region segmentation algorithm with monocular vision and sparse lidar fusion is introduced, which realizes the multi-source match of rich features in images and scale information in point clouds. In order to densify the sparse point cloud, a multi-view fusion model is constructed with multi-objective optimization, which realizes the complementation of 3-D information. Further, a 3-D reconstruction method with hybrid iteration and optimization is proposed. Unlike the method with random sampling, the proposed method can obtain the optimal solution while reducing the influence of outliers. In other to verify the effectiveness of the proposed method, a complete set of simulation and experimental systems with modular and scalable software is built. The results show that the accuracy and stability are improved by no less than 27.88% and 38.99%, respectively. Besides, the inference speed is enhanced by at least one time. The proposed method can accurately and quickly identify the geometric parameters of targets with a cubical body, which will be used for motion estimation. Moreover, it can be extended to targets with a polyhedral body.