A model-free method for attitude estimation and inertial parameter identification of a noncooperative target

Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes.(c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a model-free method for determining the attitude and inertial parameters of a noncooperative target by sequentially registering point clouds captured by a depth camera. The method combines the multiplicative extended Kalman filter and pose graph optimization to reduce measurement noise and drift error. Experimental results confirm the superiority of this method in identifying inertial parameters and its effectiveness in avoiding registration ambiguity.