Abstract: 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.

Key words: Simultaneous Localization And Mapping (SLAM), Inertial Measurement Unit (IMU), keyframe, preintegration, visual inertia tight coupling, Unmanned Aerial Vehicle (UAV) navigation

摘要： 由于嵌入式处理器算力的限制，实时性差一直是视觉惯导同时定位与建图（VI-SLAM）走向实际应用的一个亟待解决的问题，因此提出一种利用惯导测量单元（IMU）确定关键帧的实时同时定位与建图（SLAM）算法，主要分为3个线程：跟踪、局部建图和闭环。首先由跟踪线程通过IMU预积分自适应地确定关键帧，而自适应阈值由视觉惯性紧耦合优化的结果得出；然后仅对关键帧进行跟踪，避免对所有帧进行特征处理；最后利用局部建图线程在滑动窗口中通过视觉惯导光束平差法得到更加精确的无人机位姿，利用闭环线程得到全局一致的轨迹和地图。在数据集EuRoC上的实验结果表明，该算法能在不降低精度和鲁棒性的情况下显著减少跟踪线程耗时，降低VI-SLAM对计算资源的依赖。在实际飞行测试中，该算法能够较实时准确地估计出具有尺度信息的无人机飞行真实轨迹。

关键词: 同时定位与建图, 惯导测量单元, 关键帧, 预积分, 视觉惯性紧耦合, 无人机导航