Efficient pose and motion estimation of non-cooperative target based on LiDAR

In on-orbit servicing missions, autonomous close proximity operations require knowledge of the target's pose and motion parameters. Due to the lack of prior information about the non-cooperative target in an unknown environment, the pose and motion estimation of an uncooperative target is a challenging task. In this paper, a relative position and attitude estimation method is proposed using consecutive point clouds. First, a fast plane detection method is used to extract the global features of non-cooperative targets. Compared with some other local feature-detection methods, the method mentioned in this paper is faster. Then a two-stage angle adjustment method and iterative closest point algorithm are used to register the two adjacent point clouds. Finally, an unscented Kalman filter is designed to estimate the relative pose and motion parameters (velocity and angular velocity) of the target. Experiments showthat the proposed measurement method of pose and motion parameters has acceptable accuracy and good stability. (C) 2022 Optica Publishing Group

Automated summary

This paper proposes a method for relative position and attitude estimation using consecutive point clouds. The method extracts global features through fast plane detection, registers the point clouds using a two-stage angle adjustment and iterative closest point algorithm, and utilizes an unscented Kalman filter for estimating the target's pose and motion parameters.