一种可参数自标定的鲁棒视觉/惯性定位方法

以视觉/惯性里程计为代表的视觉/惯性定位方法近年来被广泛应用。传统的视觉/惯性里程计通过离线标定方法得到固定的相机畸变参数,当相机畸变参数标定不准确或发生变化时定位精度会下降。针对于此,提出了一种面向相机畸变参数在线自标定的鲁棒视觉/惯性里程计方法:首先,将相机畸变参数加入到视觉/惯性里程计的待优化变量中,推导了视觉重投影误差关于待优化变量的雅可比矩阵;然后通过因子图优化技术,实现相机畸变参数的在线自标定与载体导航信息的实时优化求解;最后,通过EuRoC数据集试验和实际试验验证了所提方法的有效性。实际试验结果表明:相对传统的视觉/惯性里程计方法,所提方法在室外开阔场景中精度提升了65.40%。

A robust visual / inertial positioning method with parameter self-calibration

The visual / inertial positioning method represented by the visual / inertial odometry has been widely used recently. The traditional visual / inertial odometry obtains the fixed distortion parameters of camera through offline calibration methods. When the calibration of camera distortion parameters is inaccurate or the camera distortion parameters are changed, the positioning accuracy will decrease. Aiming at this problem, a robust visual / inertial odometry method facing to online self-calibration of camera distortion parameters is proposed. Firstly, the camera distortion parameters are added to the variables to be optimized in the visual / inertial odometry. The Jacobian matrix of the visual reprojection error with respect to the variables to be optimized is derived. Then through the factor graph optimization method, online self-calibration of camera distortion parameters and real-time optimization solution of carrier navigation information are realized. Finally, the effectiveness of the proposed method was verified through the EuRoC dataset and realworld experiments. The real-word experiment result demonstrates that compared with the traditional visual / inertial odometry method, the accuracy of the proposed method is improved by 65. 40% in an outdoor wide open scene.