Estimating the fundamental matrix based on the end-to-end convolutional network

Yang, Ruiqi; Zhang, Junhua; Li, Bo

doi:10.1007/s10489-021-03103-w

Cited by 4 publications

(1 citation statement)

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“…Shao et al proposed a semantic flter based on a faster region-based convolutional neural network to address the outlier problem in RANSAC-based fundamental matrix computation [26]. Yang et al used the improved convolutional block attention module to ensure the estimation of an accurate fundamental matrix which is rank-2 with 7 degrees of freedom and scale invariance [30]. Although the robust algorithm has a certain anti-interference performance, when the outlier ratio exceeds a certain range, the solution accuracy will be afected.…”

Section: Introductionmentioning

confidence: 99%

Study on Optimization Method of Visual Odometry Based on Feature Matching

Song

Zhao

Ren

et al. 2022

Mathematical Problems in Engineering

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The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided.

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Section: Introductionmentioning

confidence: 99%