针对机器人视觉系统外参数标定的问题，提出了基于单目视觉ORB-SLAM的差分GPS辅助相机外参数标定方法。分析了单目视觉ORB-SLAM和GPS(Global Position System)定位数据之间的相似关系，建立了相机外参数标定的非线性最小二乘模型。基于随机采样一致性(RANSAC)，通过三点法求得模型的初始解。设计了Levenberg-Marquardt(LM)迭代算法求解出最优解，从而得到了最优的相机相对位置和姿态参数。最后，对提出的方法进行仿真和跑车试验验证。结果表明: 在试验半径为50 m时，所设计标定方法的姿态标定精度可达0.1°，位置标定精度可达0.2％。该方法标定过程简单实用，不需要外界环境的先验信息和人工干预，具有很高的精度和显著的应用价值。

An extrinsic parameter calibration method with differential GPS(Global Position System) assistant based on monocular visual ORB-SLAM(ORB-Simullaneous Location and Mapping) was proposed aimed at extrinsic parameter calibration problem of a robot vision system. Nonlinear least square models of extrinsic parameter calibration were established based on analyzing the similarity relationship between monocular visual ORB-SLAM and GPS positioning data. The initial solution of model was obtained by three-point method based on Random Sample Consensus (RANSAC), and then an optimal solution was obtained by designing Levenberg-Marquardt (LM) iterative algorithm. Thus optimal relative position and pose parameters of a camera were obtained. Simulation and traffic-running experimental verification was performed for proposed methods. The result indicates that when experimental radius was 50 m,the pose calibration precision of designed calibration method could reach 0.1° and position calibration precision could reach 0.2％. It concludes that the calibration process of the method is simple and practical. It does not need prior information of external environment and manual intervention, and has high precision and a significant application value.