Rotational motion estimation of non-cooperative target in space based on the 3D point cloud sequence

One of the key tasks for space debris removal is to estimate the rotation motion of non-cooperative targets. Many methods based on the Kalman filter have been proposed for motion estimation. However, they often need continuous tracking feature points, which conflicts with actual tracking conditions. Rodriguez formula can estimate rotation motion without continuous tracking feature points, while still facing the problem of poor estimation quality in the case of changing motion state. To overcome these problems, we propose a novel 3D point cloud sequence based algorithm to estimate the rotation motion parameters, which is composed of a phase of constructing double registration matrix and a phase of solving motion equation. With the point cloud sequence, the first phase proposes a double registration matrix to recover the local trajectory of the selected points, and the velocity information can be derived from the local trajectory. While in the second phase, the position information and velocity information of the local trajectory are substituted into the motion equation to solve the rotation axis direction and angular velocity of the non-cooperative target. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm and its potential applicability for reality.