基于松弛极线约束的月面复杂仿射变换图像匹配方法

doi:10.7527/S1000-6893.2023.28659

Abstract

Abstract:

In the process of lunar surface exploration， Yutu-2 adopts a widely spaced travel mode and takes photos of the lunar surface from a tilted perspective， resulting in a small overlap area of the lunar surface images taken between neighboring sites， and large differences in scale and rotation. In addition， due to the complex image affine transformation between sites， high texture similarity and inconsistent illumination conditions， it is easy to make errors by matching only local apparent features of images. To address the above problems， this paper proposes an image matching algorithm based on the bi-directional relaxation polar line constraint. Firstly， the approximate relative poses between stations measured by inertial guidance are introduced into the projection transform of imaging beam， and the polar line equation corresponding to the feature points of one station image in another station image is calculated. Secondly， the bi-directional polar line relaxation constraint range in the front and back station images is estimated based on the station error range， and a set of matching candidates limited within the relaxation constraint range is constructed. Then， the FLANN algorithm is used to select the feature pairs that satisfy the bi-directional relaxation constraint and have high similarity. Finally， the RANSAC algorithm is used to further reject the wrong matching points and obtain the final set of matching points. The experiments show that when dealing with the problem of inter-site large scale and rotationally transformed lunar surface image matching， the proposed algorithm can retain as many correct matching points as possible， greatly improve the correct matching rate and eliminate invalid matching points， and has a significant improvement in effect compared with ASIFT and other algorithms.

Key words: lunar surface detection, lunar surface image, affine transformation, bilateral relaxation epipolar constraint, image registration

CLC Number:

V448.2

Chuankai LIU, Zhaoxiang WANG, Junxiong LEI, Zuoyu ZHANG, Kuangang FAN, Jitao ZHANG, Xiaoxue WANG, Hailang PAN, Jianguo LIU. An epipolar relaxation constrained matching algorithm of large-affined images for lunar rover with large span distance in a single movement[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 328659-328659.

Figures/Tables 18

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References 28

1	崔平远，高锡珍，朱圣英，等. 行星着陆复杂形貌特征匹配与自主导航研究进展［J］. 宇航学报， 2022， 43（6）： 713-722.
	CUI P Y， GAO X Z， ZHU S Y， et al. Progress in complex topography feature matching and autonomous navigation for planetary landing［J］. Journal of Astronautics， 2022， 43（6）： 713-722 （in Chinese）.
2	高锡珍，汤亮，崔平远. 多视角多尺度陨石坑特征检测识别方法［J］. 宇航学报， 2022， 43（7）： 921-929.
	GAO X Z， TANG L， CUI P Y. Detection and recognition method for multiview and multiscale crater features［J］. Journal of Astronautics， 2022， 43（7）： 921-929 （in Chinese）.
3	LOWE D G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision， 2004， 60（2）： 91-110.
4	MOREL J M， YU G S. ASIFT： A new framework for fully affine invariant image comparison［J］. SIAM Journal on Imaging Sciences， 2009， 2（2）： 438-469.
5	MOHSIN ARKAH Z， IBRAHIM HASAN R， EBIS S K， et al. Image matching performance comparison using SIFT， ASIFT and moment invariants［J］. Journal of Engineering and Applied Sciences， 2019， 14（20）： 7656-7662.
6	RUBLEE E， RABAUD V， KONOLIGE K， et al. ORB： An efficient alternative to SIFT or SURF［C］∥ 2011 International Conference on Computer Vision. Piscataway： IEEE Press， 2012： 2564-2571.
7	LEUTENEGGER S， CHLI M， SIEGWART R Y. BRISK： Binary robust invariant scalable keypoints［C］∥ 2011 International Conference on Computer Vision. Piscataway： IEEE Press， 2012： 2548-2555.
8	PABLO F A， ADRIEN B， ANDREW J D. KAZE features［C］∥ECCV 2012 12th， 2012.
9	ALCANTARILLA P， NUEVO J， BARTOLI A. Fast explicit diffusion for accelerated features in nonlinear scale spaces［C］∥ Proceedings ofthe British Machine Vision Conference 2013， 2013.
10	ZITOVÁ B， FLUSSER J. Image registration methods： A survey［J］. Image and Vision Computing， 2003， 21（11）： 977-1000.
11	贾迪，朱宁丹，杨宁华，等. 图像匹配方法研究综述［J］. 中国图象图形学报， 2019， 24（5）： 677-699.
	JIA D， ZHU N D， YANG N H， et al. Image matching methods［J］. Journal of Image and Graphics， 2019， 24（5）： 677-699 （in Chinese）.
12	FISCHLER M A， BOLLES R C. Random sample consensus： A paradigm for model fitting with applications to image analysis and automated cartography［J］. Commun ACM， 1981， 24（6）： 381-395.
13	周艳青，薛河儒，马学磊，等. 基于RANSAC算法的改进特征匹配方法研究［J］. 内蒙古农业大学学报（自然科学版）， 2021， 42（6）： 87-92.
	ZHOU Y Q， XUE H R， MA X L， et al. Improved feature matching method based on ransac algorithm［J］. Journal of Inner Mongolia Agricultural University （Natural Science Edition）， 2021， 42（6）： 87-92 （in Chinese）.
14	ZHOU F， ZHONG C， ZHENG Q. Method for fundamental matrix estimation combined with feature lines［J］. Neurocomputing， 2015， 160： 300-307.
15	任继昌，杨晓东. 极线约束条件下的双目视觉点匹配策略研究［J］. 舰船科学技术， 2016， 38（3）： 121-124.
	REN J C， YANG X D. Point matching strategies of binocular vision based on epipolar line rectification［J］. Ship Science and Technology， 2016， 38（3）： 121-124 （in Chinese）.
16	赵春晖，樊斌，田利民，等. 基于极线几何的统计优化特征匹配算法［J］. 航空学报， 2018， 39（5）： 321727.
	ZHAO C H， FAN B， TIAN L M， et al. Statistical optimization feature matching algorithm based on epipolar geometry［J］. Acta Aeronautica et Astronautica Sinica， 2018， 39（5）： 321727 （in Chinese）.
17	DUSMANU M， ROCCO I， PAJDLA T， et al. D2-net： A trainable CNN for joint description and detection of local features［C］∥ 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2020： 8084-8093.
18	TIAN Y R， BALNTAS V， NG T， et al. D2D： Keypoint extraction with describe to detect approach［C］∥Asian Conference on Computer Vision. Cham： Springer， 2021： 223-240.
19	SARLIN P E， DETONE D， MALISIEWICZ T， et al. SuperGlue： Learning feature matching with graph neural networks［C］∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2020： 4937-4946.
20	唐灿，唐亮贵，刘波. 图像特征检测与匹配方法研究综述［J］. 南京信息工程大学学报（自然科学版）， 2020， 12（3）： 261-273.
	TANG C， TANG L G， LIU B. A survey of image feature detection and matching methods［J］. Journal of Nanjing University of Information Science & Technology （Natural Science Edition）， 2020， 12（3）： 261-273 （in Chinese）.
21	任晶秋，武小凯. 改进SIFT的图像匹配算法［J］. 吉林大学学报（信息科学版）， 2022， 40（4）： 672-676.
	REN J Q， WU X K. Improved SIFT algorithm for image matching［J］. Journal of Jilin University （Information Science Edition）， 2022， 40（4）： 672-676 （in Chinese）.
22	姜帅. 基于SURF算法的图像配准技术的改进［J］. 自动化应用， 2022（9）： 58-62.
	JIANG S. Improvement of image registration technology based on SURF algorithm［J］. Automation Application， 2022（9）： 58-62 （in Chinese）.
23	LIU C， DANG S W. The research and application of improved ORB feature matching algorithm［C］∥International Conference on Guidance， Navigation and Control. Singapore： Springer， 2023： 4246-4255.
24	TANG Q L， WANG X X， ZHANG M， et al. Image matching algorithm based on improved AKAZE and Gaussian mixture model［J］. Journal of Electronic Imaging， 2023， 32（2）： 023020.
25	刘传凯，王泰杰，董卫华，等. 玉兔2号月球车大间距导航成像的空间分辨率建模分析［J］. 中国科学：技术科学， 2019， 49（11）： 1275-1285.
	LIU C K， WANG T J， DONG W H， et al. Modeling and analysis of spatial resolution of images from Yutu 2 lunar rover in large-span movements［J］. Scientia Sinica （Technologica）， 2019， 49（11）： 1275-1285 （in Chinese）.
26	王泰杰，苏建华，刘传凯，等. 玉兔二号月面大间距变倾角成像的空间分辨率建模及其应用分析［J］. 中国科学：技术科学， 2020， 50（8）： 1081-1094.
	WANG T J， SU J H， LIU C K， et al. Modeling and application analysis of the pixel-spatial resolution of Yutu 2 rover with large-scale transformed images［J］. Scientia Sinica （Technologica）， 2020， 50（8）： 1081-1094 （in Chinese）.
27	LIU C K， WANG B F， YANG X， et al. Introducing projective transformations into lunar image correspondence for positioning large distance rover［C］∥ 2016 12th World Congress on Intelligent Control and Automation （WCICA）. Piscataway： IEEE Press， 2016： 1206-1211.
28	王怀超，张学全，李海丰. 一种行星车视觉导航系统的立体校正算法［J］. 宇航学报， 2017， 38（2）： 159-165.
	WANG H C， ZHANG X Q， LI H F. A stereo rectification algorithm for planetary rover visual navigation system［J］. Journal of Astronautics， 2017， 38（2）： 159-165 （in Chinese）.

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[2]	LIU Zhongjie, CAO Yunfeng, ZHUANG Likui, DING Meng. Applied Research on Airborne SAR and Optical Image Registration Based on Control Line Method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(9): 2194-2201.
[3]	SHEN Hao, LI Shuxiao, SHEN Yiping, ZHU Chengfei, CHANG Hongxing. Fast Interframe Registration Method in Aerial Videos [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(6): 1405-1413.
[4]	LIAO Chao, WANG Guijin, SHEN Yongling, HE Bei, LIN Xinggang. Aerial Video Stitching via Multi-direction Strips [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(11): 2065-2073.
[5]	YI Meng, GUO Baolong. Aerial Video Image Registration Method Based on Invariant Features and Mapping Restraint [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(10): 1872-1880.
[6]	LONG Yunli, XU Hui, AN Wei, LIN Liangkui. Spatial-temporal Fused Filtering for Infrared Clutter Suppression Based on Restricted Sequential M-estimation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(8): 1531-1541.
[7]	Wang Yunli;Zhang Xin;Gao Chao;Wang Hui;Zhang Maojun. Feature Matching Based Global Motion Estimation in Aerial Video Mosaicing [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2008, 29(5): 1218-1225.
[8]	NIU Li-pi;MAO Shi-yi;CHEN Wei. Multi-Sensor Image Registration Method Adapted for Larger Scale [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(3): 475-480.
[9]	XU Dong;AN Jin-wen. Global Motion Estimation by Fitting Optical Flow in Aerial Video Imagery [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(1): 94-97.
[10]	WANG Yue;MAO Shi-yi;CHEN Wei. A Multiresolution Algorithm for Image Registration with Partial Hausdorff Distance [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2004, 25(6): 593-597.
[11]	ZHANG Hong-yue;YANG Xue-qin;GUO Hong-tao. Aircraft Image Recognition Based on Affine Transformation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2003, 24(3): 251-254.

An epipolar relaxation constrained matching algorithm of large-affined images for lunar rover with large span distance in a single movement

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