The global localization algorithm for an indoor mobile robot based on monocular vision is significantly complex at present. To solve this problem, this study proposes a global localization method for an indoor mobile robot based on binocular vision. To ensure stable feature extraction during the motion of the indoor mobile robot using binocular vision, a calibration board-based global localization scheme is presented. The center of the calibration board is used as the localization point of the mobile robot. Based on this, to improve real-time localization and reduce the extraction range of corner points on the calibration board, the motion area detection of the mobile robot is achieved using the Gaussian mixture model background subtraction method and morphological method. Further, according to the established criterion of corner points on the calibration board, image coordinates of four corner points on the calibration board are obtained by screening the corner points extracted from the mobile robot. The coordinates of the localization point are calculated by combining the intrinsic and extrinsic parameters of the binocular camera and the global localization mathematical model, and the feasibility and effectiveness of the proposed method are verified by experiments and analysis. This provides a new idea for the global vision localization of indoor mobile robots.