The monocular visual Simultaneous Localization and Mapping (SLAM) can achieve accurate and robust pose estimation with excellent perceptual ability. However, accumulated image error over time brings out excessive trajectory drift in a GPS-denied indoor environment lacking global positioning constraints. In this paper, we propose a novel optimization-based SLAM fusing rich visual features and indoor GPS (iGPS) measurements, obtained by workshop Measurement Position System, (wMPS), to tackle the problem of trajectory drift associated with visual SLAM. Here, we first calibrate the spatial shift and temporal offset of two types of sensors using multi-view alignment and pose optimization bundle adjustment (BA) algorithms, respectively. Then, we initialize camera poses and map points in a unified world frame by iGPS-aided monocular initialization and PnP algorithms. Finally, we employ a tightly-coupled fusion of iGPS measurements and visual observations using a pose optimization strategy for high-accuracy global localization and mapping. In experiments, public datasets and self-collected sequences are used to evaluate the performance of our approach. The proposed system improves the result of absolute trajectory error from the current state-of-the-art 19.16mm (ORB-SLAM3) to 5.87mm in the public dataset and from 31.20mm to 5.85mm in the real-world experiment. Furthermore, the proposed system also shows good robustness in the evaluations.