Monocular Visual Odometer Based on Deep Learning SuperGlue Algorithm

Abstract

Aiming at the visual odometer of feature point method,the change of illumination and view angle could lead to the instability of feature point extraction,which affects the accuracy of camera pose estimation,a monocular vision odometer modeling method based on deep learning SuperGlue matching algorithm is proposed.Firstly,the feature points are obtained by SuperPoint detector,and the resulting feature points are encoded to obtainvectors containing the coordinates and descriptors of the feature points.Then the more representative descriptors are generated by attentional GNN network.We useSinkhorn algorithm to solve the optimal score distribution matrix.Finally,according to the optimal feature matching,the camera pose is restored,and the ca-mera pose is optimized by using the minimum projection error equation.Experiments show that the proposed algorithm is not only more robust to view angle and light change than the visual odometer based on ORB or SIFT,without back-end optimization,but also the accuracy of absolute trajectory error and relative pose error is greatly improved,thus the feasibility and superiority of the deep learning based SuperGlue matching algorithm in visual slam are further verified.

Keywords

• visual odometer|deep learning|feature matching|superglue|gnn