空间非合作旋转目标的模型重建与位姿优化

尹芳; 吴云

doi:10.3788/OPE.20192708.1854

您当前的位置：

首页 >

文章列表页 >

空间非合作旋转目标的模型重建与位姿优化

信息科学 | 更新时间：2020-08-13

- 空间非合作旋转目标的模型重建与位姿优化
- Model reconstruction and pose optimization of non-cooperative rotating space target
- 光学精密工程 2019年27卷第8期页码：1854-1862
- 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：
  
  [ "尹芳(1985-)，女，山东临沂人，博士研究生，2012年于中北大学获得硕士学位，主要从事机器视觉方面的研究。E-mail：yinfangcn@163.com" ]
  [ "吴云(1985-)，男，广西桂林人，博士，2014年于清华大学取得博士学位。主要从事机器视觉、光学测量敏感器设计与研制的研究。E-mail：wuy04110@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61633002)
- DOI：10.3788/OPE.20192708.1854
  中图分类号： V19
- 收稿日期：2019-02-16，
  
  录用日期：2019-3-15，
  
  纸质出版日期：2019-08-15
- 稿件说明：
移动端阅览
尹芳, 吴云. 空间非合作旋转目标的模型重建与位姿优化[J]. 光学精密工程, 2019,27(8):1854-1862.

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.
尹芳, 吴云. 空间非合作旋转目标的模型重建与位姿优化[J]. 光学精密工程, 2019,27(8):1854-1862. DOI： 10.3788/OPE.20192708.1854.

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

WANG X Q, SEKERCIOGLU A, DRUMMOND T. Vision-based cooperative pose estimation for localization in multi-robot systems equipped with RGB-D cameras[J]. Robotics , 2014, 4(1):1-22.

FASANO G, GRASSI M, ACCARDO D. A stereo-vision based system for autonomous navigation of an in-orbit servicing platform[C]. Proceedings of the AIAA Infotech @ Aero-space 2009, Seattle , USA : 2009.

CHRISTIAN J A, ROBIBSON S B, DSOUZA C N, et al .. Cooperative relative navigation of spacecraft using flash light detection and ranging sensors[J]. Journal of Guidance Control & Dynamics , 2014, 37(2):452-465.

DURRANT-WHYTE H, BAILEY T. Simultaneous localization and mapping: part Ⅰ[J]. IEEE Robotics & Automation Magazine , 2006, 13(2):99-110.

LICHTER M D, DUBOWSKY S. State, shape, and parameter estimation of space objects from range images[C]. Proceedings of the International Conference on Robotics and Automation , New Orleans , USA , 2004: 2974-2979.

SMITH R C. On the representation and estimation of spatial uncertainty[J]. The International Journal of Robotics Research , 1986, 5(4):56-68.

AUGENSTEIN S, ROCK S M. Improved frame-to-frame pose tracking during vision-only SLAM/SFM with a tumbling target[C]. Proceedings of the International Conference on Robotics and Automation , Shanghai , China , 2011: 3131-3138.

SCHNITZER F, JANSCHEK K, WILLICH G. Experimental results for image-based geometrical reconstruction for spacecraft Rendezvous navigation with unknown and uncooperative target spacecraft[C]. Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems ( IROS 2012), Vilamoura , Portugal , 2012: 5040-5045.

TWEDDLE B E. Computer Vision-Based Localization and Mapping of an Unknown, Uncooperative and Spinning Target for Spacecraft Proximity Operations [D]. USA: Massachusetts Institute of Technology, 2013.

KAESS M. Incremental smoothing and mapping[J]. IEEE Transactions on Robotics , 2008, 24(6):1365-1378.

DOR M, TSIOTRAS P. ORB-SLAM applied to spacecraft non-cooperative rendezvous[C]. Proceedings of the 2018 Space Flight Mechanics Meeting , Kissimmee , USA , 2018.

MUR-ARTAL R, MONTIEL J M M, TARDOS J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics , 2015, 31(5): 1147-1163.

杨宁, 申景诗, 张建德, 等.基于立体视觉的空间非合作航天器相对位姿自主测量[J].光学精密工程, 2017, 25(5):1331-1339.

YANG N, SHEN J SH, ZHANG J D, et al .. Autonomous measurement of relative attitude and position for spatial non-cooperative spacecraft based on stereo vision[J]. Opt. Precision Eng., 2017, 25(5): 1331-1339. (in Chinese)

刘宗明, 张宇, 卢山, 等.非合作旋转目标闭环检测与位姿优化[J].光学精密工程, 2017, 25(4):1036-1043.

LIU Z M, ZHANG Y, LU SH, et al .. Closed-loop detection and pose optimization of non-cooperation rotating targets[J]. Opt. Precision Eng., 2017, 25(5): 1036-1043. (in Chinese)

郝刚涛, 杜小平, 宋建军.空间翻滚非合作目标相对位姿估计的视觉SLAM方法[J].宇航学报, 2015, 36(6):706-714.

HAO G T, DU X P, SONG J J.Relative pose estimation of space tumbling non-cooperative target based on vision-only SLAM[J]. Journal of Astronautics , 2015, 36(6):706-714. (in Chinese)

桂力, 郑顺义, 曹姝清, 等.基于点云的非合作航天器位姿测量方法研究[J].上海航天, 2016, 33(6):122-128.

GUI L, ZHENG SH Y, CAO SH Q, et al .. Research of pose and altitude measurement for non-cooperative spacecraft based on 3D point clouds[J]. Aerospace Shanghai , 2016, 33(6):122-128. (in Chinese)

BESL P J, MCKAY N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1992, 14(2):239-256.

DYN N, HORMANN K, KIM S, et al .. Optimizing 3D triangulations using discrete curvature analysis[J]. Mathematical methods for curves and surfaces , 2001, 28(5):135-146.

RIZZINI D L. Place recognition of 3D landmarks based on geometric relations[C]. Proceedings of the International Conference on Intelligent Robots and Systems ( IROS ), Vancouver , Canada , 2017: 648-654.

KUEMMERLE R, GRISETTI G, STRASDAT H, et al .. G2o: a general framework for graph optimization[C]. Proceedings of the International Conference on Robotics and Automation , Shanghai , China , 2011: 3607-3613.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据