基于改进BRISK算法的单目视觉里程计

Monocular Visual Odometry Based on Improved BRISK Algorithm

在传统的BRISK算法中使用自定义的抽样模式来描述检测到的特征点,使用基于汉明距离的方法进行特征点匹配。BRISK的这种特征点描述与匹配的方法使得其匹配准确率不高。因此本文提出将匹配准确率较高的SURF算法与BRISK算法相结合,在BRISK特征点描述与匹配阶段使用SURF描述符和基于欧氏距离的匹配方法。实验结果表明,该算法在时间消耗下降不大的情况下,特征点匹配准确率有很大提高,且该算法具有较好的鲁棒性。

In the traditional BRISK algorithm,a custom sampling pattern is used to describe the detected feature points,and a method based on the Hamming distance is used for feature matching. This feature point description and matching method of BRISK makes the low matching accuracy. Therefore,this paper proposes to combine SURF algorithm with high accuracy of matching and BRISK algorithm,and to use SURF descriptor and Euclidean distance-based matching method in BRISK feature point description and matching stage. The experimental results show that the accuracy of feature point matching is greatly improved when the time consumption of the algorithm is not greatly reduced. At the same time,the experiment also shows that the algorithm has good robustness.