Shadow Areas Robust Matching Among Image Sequence in Planetary Landing

详细信息    查看全文

• 作者：Wei Ruoyan ; Ruan Xiaogang ; Yu Naigong ; Zhu Xiaoqing ; Lin Jia
• 关键词：Shadow areas matching ; Affine normalization ; Visual autonomous navigation ; RANSAC
• 刊名：Earth, Moon, and Planets
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：119
• 期：2-3
• 页码：95-124
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Astronomy, Observations and Techniques; Planetology; Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics); Astrophysics and Astroparticles;
• 出版者：Springer Netherlands
• ISSN：1573-0794
• 卷排序：119

文摘

In this paper, an approach for robust matching shadow areas in autonomous visual navigation and planetary landing is proposed. The approach begins with detecting shadow areas, which are extracted by Maximally Stable Extremal Regions (MSER). Then, an affine normalization algorithm is applied to normalize the areas. Thirdly, a descriptor called Multiple Angles-SIFT (MA-SIFT) that coming from SIFT is proposed, the descriptor can extract more features of an area. Finally, for eliminating the influence of outliers, a method of improved RANSAC based on Skinner Operation Condition is proposed to extract inliers. At last, series of experiments are conducted to test the performance of the approach this paper proposed, the results show that the approach can maintain the matching accuracy at a high level even the differences among the images are obvious with no attitude measurements supplied.