室外环境下基于点线特征的视觉-惯性里程计和全球卫星导航系统定位算法

Visual-Inertial Odometry and Global Navigation Satellite System Location Algorithm Based on Point-Line Feature in Outdoor Scenes

为了解决视觉-惯性里程计(VIO)在室外复杂环境中图像特征信息不足和累计误差的问题,设计提出了一种基于点线特征的单目VIO和全球卫星导航系统(VIO-GNSS)定位算法。首先,提出了一种基于几何约束的最小化策略对提取的线特征进行前后帧间约束匹配,改进了基于点线特征的单目VIO算法;在此基础上,建立了改进的VIO-GNSS松耦合定位模型,利用GNSS全局观测值对VIO位姿估计的累计误差进行校正和抑制。在KIITI数据集和实测数据集中进行了实验验证,结果表明,与几种同类算法相比,提出算法在GNSS信号良好/间断/拒止的大型室外复杂环境中均可保持良好的精度和鲁棒性,且满足驾驶定位的实时性要求。

To address the problem of insufficient image feature information and the accumulated error of visualinertial odometry(VIO)in an outdoor complex environment,this paper proposes a VIO and global navigation satellite system(GNSS)algorithm based on a combination of point and line features.First,the paper designs a minimization strategy using geometric constraints,which performs constraint matching between the front and back frames on the extracted line features.In addition,an improved VIOGNSS loose coupling model is built,in which the accumulated error in VIO pose estimation is suppressed using the global observation value of the GNSS.The experimental results in the KIITI data set and measured data set show that the proposed algorithm can maintain good accuracy and robustness compared with several similar algorithms in large outdoor complex environments with good/rejected/interrupted GNSS signals and meet the realtime requirements of driving positioning.