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

Area of Science:

• Robotics and Spacecraft Engineering
• Computer Vision and Geometric Algorithms

Background:

• Accurate relative pose determination is critical for autonomous spacecraft operations, especially for servicing uncooperative targets.
• Existing methods often require prior knowledge or dense point clouds, limiting their applicability.

Purpose of the Study:

• To develop an autonomous algorithm for determining the relative pose between a chaser spacecraft and an uncooperative target.
• To enable advanced space applications like on-orbit servicing missions.

Main Methods:

• The Congruent Tetrahedron Align (CTA) algorithm utilizes sparse, unorganized 3D point clouds from LIDAR sensors.
• It identifies relative pose by finding congruent tetrahedrons between scanned and model point clouds, enhanced by a two-level index hash table.
• The Iterative Closest Point (ICP) algorithm is employed for subsequent pose tracking.

Main Results:

• The CTA algorithm successfully provides initial pose estimation for tracking algorithms, even with arbitrary initial attitudes.
• The method demonstrates robustness against noise in simulated environments.
• Field experiments validate the effectiveness of the proposed algorithm.

Conclusions:

• The CTA algorithm offers an effective solution for autonomous relative pose determination in space applications.
• It overcomes limitations of prior methods by using sparse data and requiring no initial pose information.
• The algorithm shows significant promise for enhancing the capabilities of on-orbit servicing missions.