Abstract: Objectives: Current monocular Visual Inertial Odometry (VIO) algorithms impose stringent requirements on device motion states and visual observation conditions during initialization, which are often challenging to meet in real-world engineering applications. A fast and stable initialization method is thus essential.Methods: The proposed method utilizes the known edge length of ArUco markers to recover depth information through edge length constraints, thereby estimating the spatial relationship between the camera and the ArUco markers. Concurrently, the Inertial Measurement Unit (IMU) is initialized under stationary conditions. By incorporating the pre-calibrated extrinsic transformation between the camera and IMU, the spatial relationship between the global coordinate system and the ArUco markers is established.Results: (1) The accuracy of depth recovery at corner points is verified by a profile of the difference between the distance between two points and the depth recovery. Several sets of experiments were conducted under well-lit and low-light conditions, respectively, the average value of the error of the difference is 0.75mm. (2) The VIO positioning accuracy of the device in a certain spatial range after initialization is analyzed, and several sets of experiments show that the positioning error is 0.009m, attitude error is 0.4°. (3) Experiments are designed to verify the stability and efficiency of the method, compared to the monocular VIO initialization method of VINS-Fusion, OpenVINS and ORB-SLAM3, our method has higher stability and efficiency, and the overall trajectory of the follow-up VIO was even better.Conclusions: The proposed method demonstrates high efficiency and robustness in initialization, with notable improvements in positioning accuracy during subsequent VIO tracking.