一种基于线特征约束的无人机双目视觉SLAM算法

A Binocular Vision SLAM Algorithm for UAVs Based onLine Feature Constraints

为了解决点特征SLAM算法在低纹理环境中精度下降的问题,提出了一种基于线特征约束的无人机双目视觉SLAM算法。首先,将双目相机观测线特征分为单目相机在两帧下观测、双目相机在两帧下观测、双目相机在一帧下观测3种情况;然后,计算了3种情况下的残差块和雅可比矩阵;最后,利用所提线特征约束改进了VINS-Fusion算法。在EuRoC数据集上进行实验验证,结果表明提出的算法在数据集半数以上的场景中都有良好的表现,相较于改进前的VINS-Fusion算法定位精度平均提升13.2%。

In order to solve the accuracy degradation problem of the point feature SLAM algorithm in low texture environments a UAV binocular vision SLAM algorithm based on line feature constraints is proposed.Firstly the observation of line features by using a binocular camera is divided into three situations that is a monocular camera observing in two frames a binocular camera observing in two frames and a binocular camera observing in one frame.Secondly the residual block and Jacobian matrix in the three situations are calculated respectively.Finally the line feature constraints proposed in this paper are used to improve the VINS-Fusion algorithm.The experiments are conducted on the EuRoC dataset and the results show that the proposed algorithm exhibits good performance in over half scenarios of the dataset and has a 13.2%improvement on average in positioning accuracy compared with the VINS-Fusion algorithm before improvement.