We propose a fully autonomous micro aerial vehicle with onboard sensors to achieve simultaneous three-dimensional localization and dense reconstruction. Based on the ORB-SLAM system, a visual-inertial simultaneous localization and mapping system is proposed based on the extended Kalman filter, which improves the robustness and accuracy of the system to meet the requirements of micro aerial vehicle autonomous flight. Since sparse feature point maps created by the ORB-SLAM system can't be used for micro aerial vehicle obstacle avoidance and navigation, a stereo camera is used to propose an improved method of building maps from sparse maps to dense octree maps. The experiment evaluation with EuRoC dataset shows that the proposed algorithm improves the precision of open keyframe-based visual-inertial algorithm by one time. The proposed algorithm is applied to the quadrotor autonomous flight platform, and the fully autonomous flight and dense map construction is achieved by relying on on-board sensors and processors. The effectiveness and robustness of the proposed algorithm are verified.