Due to the limitation of the computational power of embedded processors,the poor real-time performance has always been an urgent problem to be solved in the practical applications of Visual Inertial Simultaneous Localization And Mapping(VI-SLAM). Therefore,a real-time Simultaneous Localization And Mapping(SLAM)with keyframes determined by Inertial Measurement Unit(IMU)was proposed,which was mainly divided into three threads:tracking,local mapping and loop closing. Firstly,the keyframes were determined adaptively by the tracking thread through the IMU pre-integration, and the adaptive threshold was derived from the result of the visual inertia tight coupling optimization. Then,only the keyframes were tracked,thereby avoiding the feature processing to all frames. Finally,a more accurate Unmanned Aerial Vehicle(UAV)pose was obtained by the local mapping thread through the visual inertial bundle adjustment in the sliding window,and the globally consistent trajectory and map were output by the loop closing thread. Experimental results on the dataset EuRoC show that the algorithm can significantly reduce the tracking thread time consumption without loss of precision and robustness,and reduce the dependence of VI-SLAM on computing resources. In the actual flight test,the true trajectory of the drone with scale information can be estimated accurately by the algorithm in real time.