Real-Time Visual-Inertial SLAM Based on Adaptive Keyframe Selection for Mobile AR Applications

Simultaneous localization and mapping (SLAM) technology is used in many applications, such as augmented reality (AR)/virtual reality, robots, drones, and self-driving vehicles. In AR applications, rapid camera motion estimation, actual size, and scale are important issues. In this research, we introduce a real-time visual-inertial SLAM based on an adaptive keyframe selection for mobile AR applications. Specifically, the SLAM system is designed based on the adaptive keyframe selection visual-inertial odometry method that includes the adaptive keyframe selection method and the lightweight visual-inertial odometry method. The inertial measurement unit data are used to predict the motion state of the current frame and it is judged whether or not the current frame is a keyframe by an adaptive selection method based on learning and automatic setting. Relatively unimportant frames (not a keyframe) are processed using a lightweight visual-inertial odometry method for efficiency and real-time performance. We simulate it in a PC environment and compare it with state-of-the-art methods. The experimental results demonstrate that the mean translation root-mean-square error of the keyframe trajectory is 0.067 m without the ground-truth scale matching, and the scale error is 0.58 with the EuRoC dataset. Moreover, the experimental results of the mobile device show that the performance is improved by 34.5-53.8 using the proposed method.