| 应用灰度特征的行星陨石坑自主检测方法与着陆导航研究 |
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| 引用本文： | 陈建清,朱圣英,崔祜涛,崔平远.应用灰度特征的行星陨石坑自主检测方法与着陆导航研究[J].宇航学报,2014,35(8):908-915. |
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| 作者姓名： | 陈建清 朱圣英 崔祜涛 崔平远 |
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| 作者单位： | 1.哈尔滨工业大学深空探测基础研究中心，哈尔滨 150080；
2. 北京理工大学宇航学院，北京 100081；
3.飞行器动力学与控制教育部重点实验室，北京 100081 |
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| 基金项目： | 国家重点基础研究发展计划(2012CB720000）；国家自然科学基金(60874094）；高等学校博士学科点专项科研基金(20111101110001) |
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| 摘 要： | 针对陨石坑阴影不明显的行星着陆导航图像，提出了基于图像灰度值特征的陨石坑自主检测方法，通过兴趣区快速确定陨石坑边缘分布，解决了一般陨石坑检测方法对图像太阳高度角的依赖问题。利用检测出的陨石坑视线信息，采用非线性预测滤波方法估计着陆器着陆阶段的位置和姿态；鉴于视觉着陆导航对照了场景状况的高度依赖性，提出了一种利用分形理论和相邻点分级方法的天体随机地景建模方法。最后验证了所提方法的有效性。
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| 关 键 词： | 陨石坑自主检测 灰度特征 精确着陆 自主光学导航 地景建模 |
| 收稿时间： | 2013-05-06 |
Automated Crater Detection Method Using Gray Value Features and Planet Landing Navigation Research |
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| CHEN Jian qing,ZHU Sheng ying,CUI Hu tao,CUI Ping yuan.Automated Crater Detection Method Using Gray Value Features and Planet Landing Navigation Research[J].Journal of Astronautics,2014,35(8):908-915. |
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| Authors: | CHEN Jian qing ZHU Sheng ying CUI Hu tao CUI Ping yuan |
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| Institution: | 1.Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150080,China；
2.School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China；
3.Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China |
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| Abstract: | An autonomous crater detection method based on the image gray value feature is proposed in this paper. In this method, region of interest (ROI) is used to identify crater’s edge distribution from real planet landing navigation images conveniently, thus avoiding dependence of image on solar elevation. Then an algorithm for determining both vehicle attitude and position by use of the nonlinear predictive filter is also described. Considering that landing terrain plays a significant role in celestial body landing navigation, a method of terrain map simulation is proposed. By sorting surface points into levels, new points completely depend on their higher level neighbors according to the fractal theory. A celestial body random terrain map modeling method is proposed. Finally the paper presents the validation of the algorithms in the end. |
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| Keywords: | Automated crater detection Gray value features Precise landing Autonomous optical navigation Terrain map modeling |
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