基于深度学习SuperGlue算法的单目视觉里程计

Monocular Visual Odometer Based on Deep Learning SuperGlue Algorithm

基于特征点法的视觉里程计中,光照和视角变化会导致特征点提取不稳定,进而影响相机位姿估计精度,针对该问题,提出了一种基于深度学习SuperGlue匹配算法的单目视觉里程计建模方法。首先,通过SuperPoint检测器获取特征点,并对得到的特征点进行编码,得到包含特征点坐标和描述子的向量;然后,通过注意力GNN网络生成更具代表性的描述子,并创建M×N型得分分配矩阵,采用Sinkhorn算法求解最优得分分配矩阵,从而得到最优特征匹配;最后,根据最优特征匹配进行相机位姿恢复,采用最小化投影误差法进行相机位姿优化。实验结果表明,在无后端优化的条件下,该算法与基于ORB或SIFT算法的视觉里程计相比,不仅对视角和光线变化更鲁棒,而且其绝对轨迹误差和相对位姿误差的精度均有显著提升,进一步验证了基于深度学习的SuperGlue匹配算法在视觉SLAM中的可行性和优越性。

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.