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Point Cloud Based Relative Pose Estimation of a Satellite in Close Range

机译：基于点云的近距离卫星相对姿态估计

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摘要

Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective.
机译：确定卫星的相对姿态在空间交会和在轨维修任务中至关重要。关键问题是在追赶者身上采用合适的传感器以及有效的姿态估计技术。本文旨在通过使用闪光激光雷达传感器生成的点云数据，基于已知模型估算目标卫星的近距离姿态。提出了一种基于模型的姿态估计新方法。它包括一种通过直接处理密集点云数据而基于全局最优搜索的快速可靠的姿态初始获取方法，以及一种基于迭代最近点算法的姿态跟踪方法。此外，本文提出了一种仿真系统，以评估传感器的性能并生成仿真的传感器点云数据。它还提供了测试目标的真实姿态，以便可以量化姿态估计误差。为了研究该方法的有效性和可达到的姿态精度，进行了数值模拟实验。结果证明了直接使用点云和较大姿态变化进行运算的算法能力。此外，进行了现场测试实验，结果表明该方法是有效的。

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