基于快速定向和旋转图像的相机姿态估计鲁棒图像匹配

Junqi Bao, Xiaochen Yuan, C. Lam
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引用次数: 0

摘要

提出了一种新的基于点云分割的相机姿态估计图像匹配方法。采用定向快速旋转算法(ORB)提取关键点,然后根据匹配的点云平面提取关键点。基于深度图像对点云平面进行分割,然后根据平面间质心的距离进行匹配。根据平面三维坐标的距离生成平面上的假定对应的关键点,并根据假定对应的关键点进一步匹配关键点的描述符。作为附加的约束条件,三维空间中的空间相对位置解决了某些场景中每个关键点的描述符过于相似而导致不匹配的问题。实验结果表明,与现有的匹配方法进行了比较,说明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Image Matching for Camera Pose Estimation Using Oriented Fast and Rotated Brief
This paper presents a novel image matching method for camera pose estimation based on point cloud segmentation. The Oriented Fast and Rotated Brief (ORB) is employed to extract the key points, which are then extracted based on matched point cloud planes. The point cloud planes are segmented based on the depth image, and then matched by the distance of the centroid between planes. The putative corresponding key points on the planes are generated based on the distance of their 3-D coordinates and the descriptors of the key points are further matched based on the putative corresponding key points. As an additional constraint, the spatial relative position in 3-D spaces solves the problem that the descriptors of each key point in some scenarios are too similar which may lead to a mismatch. According to the experimental results, the superiority of the proposed approach is illustrated by comparing with the existing matching methods.
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