Model reconstruction and pose optimization of non-cooperative rotating space target

Fang YIN; Yun WU

doi:10.3788/OPE.20192708.1854

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Model reconstruction and pose optimization of non-cooperative rotating space target

Information Sciences | 更新时间：2020-08-13

- Model reconstruction and pose optimization of non-cooperative rotating space target
- Optics and Precision Engineering Vol. 27, Issue 8, Pages: 1854-1862(2019)
- 作者机构：
  
  1.北京航空航天大学机械工程及自动化学院，北京 100191
  2.北京控制工程研究所，北京 100190
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192708.1854
  CLC： V19
- Received：16 February 2019，
  
  Accepted：15 March 2019，
  
  Published：15 August 2019
- 稿件说明：
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Fang YIN, Yun WU. Model reconstruction and pose optimization of non-cooperative rotating space target[J]. Optics and precision engineering, 2019, 27(8): 1854-1862.
DOI：

Fang YIN, Yun WU. Model reconstruction and pose optimization of non-cooperative rotating space target[J]. Optics and precision engineering, 2019, 27(8): 1854-1862. DOI： 10.3788/OPE.20192708.1854.

摘要

针对模型未知的空间非合作旋转目标的模型重建和位姿估计问题，利用激光雷达采集的3D点云，提出一种基于位姿图优化的SLAM技术框架，以解决跟踪过程中产生的累积误差问题。首先，根据迭代最近点(Iterative Closest Point，ICP)算法计算相邻关键帧之间的相对位姿信息，通过位姿跟踪方法获得当前关键帧的位姿，由此构建跟踪航天器的相对位姿图；采用GLAROT-3D(Geometric LAndmark relations ROTation-invariant 3D)全局描述子检测闭环，并将闭环约束添加到位姿图中；最后采用基于位姿图优化的方法进行位姿调整，并更新模型点云。在仿真实验中，噪声标准差达到100 mm时，姿态测量误差小于2°；在地面实验中，姿态测量误差小于2.5°，并较好地重建了目标的点云模型，算法的精度及抗噪声能力基本满足非合作目标相对位姿测量的任务需求。

Abstract

For the model reconstruction and pose estimation of non-cooperative rotating space targets with unknown model

the technology of graph-based optimization SLAM was applied to reduce the cumulative error in the pose tracking process by using 3D point clouds acquired through LiDAR. First

the relative pose between adjacent key frames was calculated by the Iterative Closest Point (ICP) algorithm

and the pose of the current key frame was obtained by the pose tracking method

thereby constructing the pose graph of the chaser spacecraft. Meanwhile

the global 3D signature GLAROT-3D (Geometric LAndmark relations ROTation-invariant 3D) was used to detect the loop closure

and adding the closed-loop constraint to the pose graph. Finally

the method based on pose graph optimization was used to adjust the pose and update the model point cloud. Experimental results show that in the simulation test

when the noise amplitude reaches 100mm

the attitude measurement error is less than 2°. In the field experiment

the attitude measurement error is less than 2.5°

and the target point cloud model is well reconstructed. Hence

the accuracy and the anti-noise ability of the proposed method can satisfy the mission requirements for the relative pose measurement of non-cooperative target.

关键词

Keywords

references

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