You Hairong, Xie Yang
Ministry of Information Technology, China Minsheng Bank, No. 2 Fuxingmen Inner Street, Xicheng District, 100032 Beijing, China.
Mobile Department, Xiaomi Technology Co., Ltd., No. 33 Xierqi Middle Road, Haidian District, 100085 Beijing, China.
Rev Sci Instrum. 2024 Aug 1;95(8). doi: 10.1063/5.0214966.
In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars. We use RANSAC to obtain the initial inlier set and the SC algorithm to filter RANSAC's outliers and then use the filtered inliers as the final inlier set. In order to verify the effectiveness of the algorithm, it shows the matching effect from three angles: camera translation, rotation, and rotation and translation. SC-RANSAC is also compared with RANSAC, graph-cut RANSAC, and marginalizing sample consensus by using two different types of datasets. Finally, we select three representative pictures to test the robustness of the SC-RANSAC algorithm. The experimental results show that SC-RANSAC can effectively and reliably eliminate mismatches in the initial matching results; has a high inlier rate, real-time performance, and robustness; and can be effectively applied in the environment of autonomous driving.
在当今的大数据时代，随着物联网（IoT）技术的发展以及自动驾驶趋势的盛行，视觉信息呈现出爆发式增长，但大多数图像匹配算法存在准确率低、内点率低等问题，导致信息不足。为了解决自动驾驶领域中图像匹配准确率低和内点率低的问题，本研究创新性地将数据分析领域的谱聚类（SC）应用于自动驾驶领域的图像匹配，提出了一种基于SC和随机抽样一致性（RANSAC）的新图像匹配算法“SC-RANSAC”。本研究中的数据集是基于自动驾驶汽车的单目摄像头收集的。我们使用RANSAC获得初始内点集，并用SC算法过滤RANSAC的外点，然后将过滤后的内点作为最终的内点集。为了验证算法的有效性，从相机平移、旋转以及旋转和平移三个角度展示了匹配效果。还使用两种不同类型的数据集将SC-RANSAC与RANSAC、图割RANSAC和边缘化样本一致性进行了比较。最后，我们选择三张具有代表性的图片来测试SC-RANSAC算法的鲁棒性。实验结果表明，SC-RANSAC能够有效且可靠地消除初始匹配结果中的误匹配；具有高内点率、实时性能和鲁棒性；并且能够有效地应用于自动驾驶环境中。
