Global Navigation Satellite System (GNSS), Strapdown Inertial Navigation System (SINS) and visual sensors 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 decrease 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 algorithm in this paper has higher accuracy and better robustness than the traditional algorithm without robust kernel function, outlier elimination strategy and chi-square test, and the algorithm with other robust kernel functions. It can greatly improve the positioning accuracy and robustness of GNSS/SINS/visual navigation. In large scale environment, there is no large drift errors, and absolute pose root mean square error is 0.735m, and the standard deviation of absolute pose error is 0.336m.