目的 针对传统的图像特征点匹配算法数据量大，计算耗时长的特点，提出一种实时鲁棒的特征点匹配算法（RRM）。方法 通过微分操作确定图像的边缘区域，找出边缘区域中很有可能成为特征点的锚点，即梯度局部最大的点。对于每个检测出来的特征点，通过计算Intensity Centroid来确定特征点的方向，并且使用改进的Brief来对特征点进行描述，使之具有旋转不变性。最后，结合Hamming距离和对称匹配检验对特征点进行匹配。结果 本文算法与多种算法进行对比，在光照发生变化的情况下，RRM表现出明显的优越性和稳定性，正确匹配率达到83%左右，而其他算法的准确匹配率随着光照的变暗明显下降；在视角、尺度和旋转变化条件下，RRM也具有较高的准确匹配率。结论 实验结果表明，RRM在保证匹配精度的前提下，有效地解决了传统特征点匹配方法中的缺点。因此，本文算法能更好地应用于图像拼接、目标跟踪和对象识别等领域。

Objective Traditional image feature points matching algorithms of large amounts of data bring large computation burdens, particularly for real-time systems, such as visual odometry, or low-power devices, such as cellphones. This condition has led to an intensive search for replacements with lower computation cost. Method This paper proposes a robust real-time feature-point matching algorithm designated RRM. It first determines the edge area of the image by deviation operation, and then finds the anchor point or the gradient local maximum points, which are likely to be the feature points in the edge area. Next, it determines the direction of feature points by calculating Intensity Centroid, and then describes the feature points based on the improved Brief. Finally, it matches the feature points by combining Hamming distance with symmetrical match. Result Compared with a variety of feature-point matching algorithms, the proposed algorithm attains a higher success rate of 83% for images with complex backgrounds such as illumination changes, viewpoint changes, and scaling and rotation changes. The proposed algorithm is superior to and more stable than others. Conclusion Experimental results indicate that the proposed algorithm solves the limitation of traditional feature-point matching effectively, without losing accuracy. The proposed algorithm can be used for image stitching, object tracking, and object recognition.