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Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target

机译：基于稀疏无组织点云的不合作空间目标相对姿态估计

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摘要

This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method.
机译：本文提出了一种自主算法，以确定追赶航天器与不合作的空间目标之间的相对姿态，这对于高级空间应用（例如在轨服务任务）至关重要。所提出的方法名为Congruent Tetrahedron Align（CTA）算法，它使用LIDAR传感器获取的非常稀疏的无组织3D点云，并且不需要任何先前的姿态信息。该方法的核心是在已知模型的基础上，通过在扫描点云和模型点云中寻找全等四面体来确定相对姿态。建立二级索引哈希表可加快搜索速度。此外，迭代最近点（ICP）算法用于CTA之后的姿势跟踪。为了以任意初始姿态评估该方法，提出了一个仿真系统。具体地，证明了所提出的方法的性能，该性能提供了跟踪算法所需的初始姿势，以及其抗噪声的鲁棒性。最后，进行了现场实验，结果证明了该方法的有效性。

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