Image registration is one of the most fundamental steps in optical mapping from mobile platforms. Lately, image registration is performed by detecting salient points in two images and matching their descriptors. Robust methods [such as Random Sample Consensus (RANSAC)] are employed to eliminate outliers and compute the geometric transformation between the coordinate frames of images, typically a homography when the images contain views of a flat area. However, the image registration pipeline can sometimes provide a sufficient number of wrong inliers within the error bounds even when images do not overlap at all. Such mismatches occur especially when the scene has repetitive texture and shows structural similarity. Such pairs prevent the trajectory (thus, a mosaic) from being estimated accurately. In this paper, we propose to utilize closed-loop constraints for identifying mismatches. Cycles appear when the camera revisits an area that was imaged before, which is a common practice especially for mapping purposes. The proposed method exploits the fact that images forming a cycle should have an identity mapping when all the homographies between images in the cycle are multiplied. Our proposal obtains error statistics for each matched image pair extracting several cycle bases. Then, by using a previously trained classifier, it identifies image pairs by comparing error histograms. We present experimental results with different image sequences.

中文翻译：

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基于闭环误差直方图的不匹配图像识别，用于基于特征的光学映射

图像配准是从移动平台进行光学映射的最基本步骤之一。最近，通过检测两个图像中的显着点并匹配它们的描述符来执行图像配准。鲁棒的方法[例如随机样本共识（RANSAC）]用于消除离群值，并计算图像坐标系之间的几何变换，通常是当图像包含平坦区域的视图时应采用单应性。但是，即使图像根本不重叠，图像配准管线有时也可以在误差范围内提供足够数量的错误线。尤其是当场景具有重复纹理并显示结构相似性时，会发生这种不匹配。这样的对妨碍了准确地估计轨迹（因此是马赛克）。在本文中，我们建议利用闭环约束来识别失配。当照相机重新访问之前成像的区域时，会出现循环，这是一种常见做法，尤其是对于制图目的。所提出的方法利用以下事实：当将周期中的图像之间的所有单应性相乘时，形成周期的图像应具有身份映射。我们的建议为每个匹配的图像对获取了错误统计信息，并提取了多个循环基准。然后，通过使用之前训练有素的分类器，它通过比较误差直方图来识别图像对。我们用不同的图像序列展示实验结果。所提出的方法利用以下事实：当将周期中的图像之间的所有单应性相乘时，形成周期的图像应具有身份映射。我们的建议为每个匹配的图像对获取了错误统计信息，并提取了多个循环基准。然后，通过使用之前训练有素的分类器，它通过比较误差直方图来识别图像对。我们用不同的图像序列展示实验结果。所提出的方法利用以下事实：当将周期中的图像之间的所有单应性相乘时，形成周期的图像应具有身份映射。我们的建议为每个匹配的图像对获取了错误统计信息，并提取了多个循环基准。然后，通过使用之前训练有素的分类器，它通过比较误差直方图来识别图像对。我们用不同的图像序列展示实验结果。