视觉定位系统中,图像匹配的精度直接影响整个定位系统的精度,针对图像匹配中存在的误匹配率较高等问题,提出了一种基于多层次FAST(MFAST)和优化采样的随机采样一致性(RANSAC)算法的图像匹配算法。首先采用MFAST算法提取角点,运用加速稳健特征算法确定主方向生成特征描述符;然后在基于RANSAC的框架下,利用改进的加权K-最近邻分类方法选取新的样本集合计算出最佳模型参数,从而剔除误匹配点。在真实场景下进行实验,结果表明,与传统算法相比,该算法能高效剔除误匹配点,提高图像的匹配精度,且满足实时性要求。

In visual positioning system, the accuracy of image matching directly affects the accuracy of the whole positioning system. In this paper, an image matching algorithm based on multi-level FAST (MFAST) and random sampling consistency (RANSAC) algorithm with optimized sampling is proposed for solving the problem of high mismatch rate in image matching. First, the MFAST algorithm is used to extract the corner points, and the speeded up robust feature (SURF) algorithm is used to determine the main direction to generate feature descriptors. Then, in the framework based on RANSAC algorithm, improved weighted K-nearest neighbor (PTM-DWKNN) classification method is utilized to calculate the best model parameters by selecting a new sample set, thereby eliminating the mismatch points. Simulation results confirm the superiority of the proposed method in comparison with the classic ones in real-world scenarios. The proposed algorithm can effectively eliminate mismatched points, improve the matching accuracy of the image, and meet the real-time requirements.