To solve the problem of missing absolute heading data and attitude drift in the visual-inertial navigation system, and to enhance its positioning accuracy, an adaptive visual-inertial-geomagnetic tightly integrated positioning system was developed for environments with unknown magnetic fields. Initially, the calibration process for the internal and external parameters of standard tri-axis magnetometers is detailed. Following this, a strategy for generating global and frame-to-frame constrained residuals from geomagnetic data is outlined. The system dynamically adjusts fusion weights based on variations in magnetic intensity and employs a nonlinear optimization approach for the visual-inertial-geomagnetic integration to estimate its motion state accurately. Outdoor tests conducted on a university campus demonstrated that the system remains stable amidst magnetic disturbances from buildings and vehicles, achieving positioning accuracy better than 0.8% (RMSE). When compared to VINS, this system reduces position error by an average of 24%, showcasing impressive real-time capabilities. Incorporating magnetometers and adaptive fusion techniques significantly boosts the performance of existing visual-inertial navigation systems, offering reliable real-time positioning for autonomous systems.