GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System,GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System,SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS／SINS／视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS／SINS／视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

GNSS/SINS/visual navigation robust algorithm

Global Navigation Satellite System (GNSS),Strapdown Inertial Navigation System (SINS) and visual sensors can complement each other,and their information fusion can obtain high-precision,drift-free navigation and positioning information.Aiming at the problem that GNSS／SINS／vision fusion navigation is vulnerable to the impact of motion speed,light change,occlusion,etc.,which leads to the decline of navigation positioning accuracy and robustness,this paper adds the SoftLone robust kernel function to the cost function of the graph optimization framework,and sets the gross error test procedure of the measured value to reduce the negative impact of outliers.Further,the chi-square test is performed on the calculated residuals of the measured value,and the weight of the over-limit residual is reduced to improve the accuracy and robustness of the system.The experimental results show that the proposed algorithm has higher accuracy and better robustness than traditional algorithm without robust kernel function,outlier elimination strategy and chi-square test,and algorithm with other robust kernel functions.It can greatly improve the positioning accuracy and robustness of GNSS／SINS／visual navigation.In large scale scenario,there is no large drift errors,and root mean square error and standard deviation of absolute pose are 0.735 m and 0.336 m,respectively.