Facing the lunar surface survey of the Lunar Exploring Engineering, the paper summarizes the environment sensing technology based on vision image. For the image matching is the most important step in the process of the lunar exploring images, the accuracy and speed of the matching method is the key problem of the lunar exploring, which play an important role in the rover auto navigating and tele-operating. To conquer difficult problem that there are significant illumination variation of the imaging, lack of image texture, and non-uniform distribution of the image texture, the huge change of the disparity for the prominent target in the scene, in the image process Engineering, the image matching method is proposed which divided the whole image into <i>M×N</i> regions, and each region employs the Forstner algorithm to extract features， by which the semi-uniform distribution features of whole image and avoiding of the features gathering is achieved. According to the semi- uniform distribution features, the Sift and Least Square Matching method are used to realize accurate image matching. Guided by the matched features of the first step, the locale plane is detected to restrict dense image registering. The matching experiments show that the method is effective to deal with the image captured by the lunar exploring rover, that has large variation of illumination and lacking of image texture. The robustness and high accuracy of the method is also proved. The method satisfied the request of the lunar surface exploring.
Proc. SPIE. 6833, Electronic Imaging and Multimedia Technology V
KEYWORDS: Lithium, 3D acquisition, 3D image reconstruction, Detection and tracking algorithms, Aerospace engineering, Feature extraction, 3D metrology, Reconstruction algorithms, Algorithm development, 3D image processing
An algorithm for image matching is proposed, which uses both epipolar and homography constraints. At the first step,
the Forstner algorithm is employed for features extraction. The features description and matching method of SIFT are
used to find a group of original correspondences, then the correspondences are refined by LSM(Least Square Matching).
With the refined correspondences the RANSAC algorithm estimates the fundamental matrix robustly and the more
accurate correspondences with less outliers are gotten, which are called as correspondences candidates. As the features
extracted from the image are all the edge inflexions, texture nodes with maximal intensity or corners of the objects in the
3D world. The features which are adjacent can form a local plane or quasi-plane. So the homography constraint is
proposed for image matching. At the second step, the corresponding features seeds around the feature to be matched are
recognized from the correspondences, which associate with a real 3-D scene plane or quasi-plane. Then with the seed
correspondences the local homography matrix is computed. At last, under the guide of the local homography matrix, the
coarse position of the target feature on the opponent image is found, then with the constraints of the epipolar line and the
coarse position, the normal correlation and LSM matching methods are employed to match the features accurately. The
algorithm searches for the corresponding feature only in a very small region and works quickly. Experimental results
show that the algorithm is efficient and it improves the robustness and accuracy of the automatic image matching.