imagefeaturematching is an important part of visual odometry. In order to improve the accuracy of feature point matching for visual odometry, an accurate algorithm combining the pyramid feature optical flow and corn...
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imagefeaturematching is an important part of visual odometry. In order to improve the accuracy of feature point matching for visual odometry, an accurate algorithm combining the pyramid feature optical flow and corner features based on imagefeaturematching is proposed. Firstly, the Oriented features from Accelerated Segment Test and Rotated BRIEF (ORB) algorithm is used to extract the imagefeature points quickly. Secondly, the local feature window is utilised to calculate the displacement vector of the imagefeature points and the pyramid Lucas-Kanade feature optical flow is used to track feature points. Then, to solve the problem of matching alignment and feature loss, the K nearest neighbour radius search is used as feature filter to remove the confused matching. Finally, the Random Sample Consensus algorithm is introduced to eliminate redundant mismatch points and improved the matching rate. The comparison of experimental data shows that the proposed algorithm can get high matching rate. Compared with the traditional ORB featurematchingalgorithm, the algorithm had a significant improvement in real time and imagefeaturematching accuracy.
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