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一种面向空间飞行器视觉导航的椭圆检测算法

陆婷婷 邓志均 顾鑫 费智婷 吴新峰 王华

陆婷婷,邓志均,顾鑫,等. 一种面向空间飞行器视觉导航的椭圆检测算法[J]. 北京航空航天大学学报,2023,49(4):853-868 doi: 10.13700/j.bh.1001-5965.2021.0363
引用本文: 陆婷婷,邓志均,顾鑫,等. 一种面向空间飞行器视觉导航的椭圆检测算法[J]. 北京航空航天大学学报,2023,49(4):853-868 doi: 10.13700/j.bh.1001-5965.2021.0363
LU T T,DENG Z J,GU X,et al. An ellipse detection algorithm for spacecraft optical navigation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):853-868 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0363
Citation: LU T T,DENG Z J,GU X,et al. An ellipse detection algorithm for spacecraft optical navigation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):853-868 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0363

一种面向空间飞行器视觉导航的椭圆检测算法

doi: 10.13700/j.bh.1001-5965.2021.0363
详细信息
    通讯作者:

    E-mail:tingtingspring@163.com

  • 中图分类号: V419+.9

An ellipse detection algorithm for spacecraft optical navigation

More Information
  • 摘要:

    基于椭圆特征的空间飞行器视觉导航技术是一种新颖的高精度空间探测自主导航方法,如何对空间目标的环形边缘进行精准提取和高效拟合是实现空间飞行器视觉导航的必要条件。针对该问题,提出一种面向空间飞行器视觉导航的椭圆检测算法。利用多项式逼近导航图像连续边缘段的方式提取椭圆弧段;通过基于极大似然假设检验理论构建的模型选择判据,对来自同一个椭圆的椭圆弧段进行准确合并;对合并后的椭圆弧段进行拟合,得到空间飞行器视觉导航的椭圆检测结果。大量的仿真实验表明:与传统的椭圆检测算法相比,所提算法具有较高的精度和更高的鲁棒性,可以广泛应用于空间飞行器视觉导航图像椭圆检测,为空间飞行器视觉导航算法提供精准的二次曲线输入。

     

  • 图 1  本文算法总体框架

    Figure 1.  Overall framework of the proposed algorithm

    图 2  点到二次曲线几何距离计算示意图

    Figure 2.  Schematic diagram of calculating geometrical distance from point to conic

    图 3  空间导航图像预处理算法基本流程

    Figure 3.  Basic flowchart of spacecraft navigation image pre-processing algorithm

    图 4  椭圆弧段提取示例

    Figure 4.  Instances of elliptic curve extraction

    图 5  椭圆弧段合并必要条件示意图

    Figure 5.  Examples of necessary condition of elliptic curves mergence

    图 6  基于极大似然假设检验的椭圆弧段合并的模型

    Figure 6.  Mergence models of elliptic curves based on maximum likelihood ratio test

    图 7  四种点到二次曲线几何距离计算算法平均计算时间

    Figure 7.  Mean calculation time of four geometric distance calculation algorithms from point to curve

    图 8  四种点到二次曲线几何距离计算算法平均收敛率

    Figure 8.  Mean rate of convergence of four geometric distance calculation algorithms

    图 9  本文几何距离计算算法的典型计算结果

    Figure 9.  Typical calculation results of the proposed geometric distance algorithms

    图 10  数据集1测试结果

    Figure 10.  Testing results on dataset1

    图 11  数据集2测试结果

    Figure 11.  Testing results on dataset 2

    图 12  交叉椭圆数据集中椭圆示例

    Figure 12.  Instances of ellipses in crossing ellipse dataset

    图 13  交叉椭圆数据集上椭圆检测结果图例

    Figure 13.  Examples of ellipse detection results on crossing ellipse dataset

    图 14  交叠椭圆数据集中椭圆示例

    Figure 14.  Instances of ellipses in occluded ellipse dataset

    图 15  交叠椭圆数据集上椭圆检测结果图例

    Figure 15.  Examples of ellipse detection results on occluded ellipse dataset

    图 16  椒盐噪声椭圆数据集中椭圆示例

    Figure 16.  Instances of ellipses in ellipse dataset with impulsive noise

    图 17  椒盐噪声数据集上椭圆检测结果图例

    Figure 17.  Examples of ellipse detection results on ellipse dataset with impulsive noise

    图 18  真实数据集上椭圆检测结果图例

    Figure 18.  Examples of ellipse detection results on real dataset

    表  1  椭圆检测算法对几何参数鲁棒性测试数据集

    Table  1.   Dataset testing robustness of ellipse detection algorithm to geometric parameters

    数据集图像数量图像尺寸/像素半长轴a/像素半短轴b/像素长短轴之比a/b旋转角θ/(°)
    数据集19100400×400100区间[1,100],步长为1不需设定,由ab确定区间[0,90],步长为1
    数据集210000400×400区间[1,100],步长为1不需设定,由aa/b确定[0.01,1],步长为0.0160
    下载: 导出CSV

    表  2  不同算法在真实数据集上的实验结果

    Table  2.   Experimental results of different algorithms on real dataset

    算法RPF1平均执行时间/s
    本文算法 0.81 0.84 0.83 77.75
    Alex
    算法[29]
    0.78 0.78 0.78 124.64
    Prasad
    算法[27]
    0.74 0.75 0.75 46.84
    Yasu
    算法[28]
    0.53 0.53 0.53 110.65
    Mai
    算法[12]
    0.51 0.55 0.53 97.92
    RHT
    算法[9]
    0 0 0 80.31
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-30
  • 录用日期:  2022-01-08
  • 网络出版日期:  2022-03-09
  • 整期出版日期:  2023-04-30

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