Abstract
Simultaneous localization and mapping (SLAM) is emerging as a prominent issue in computer vision and next-generation core technology for robots, autonomous navigation and augmented reality. In augmented reality applications, fast camera pose estimation and true scale are important. In this paper, we present an adaptive monocular visual–inertial SLAM method for real-time augmented reality applications in mobile devices. First, the SLAM system is implemented based on the visual–inertial odometry method that combines data from a mobile device camera and inertial measurement unit sensor. Second, we present an optical-flow-based fast visual odometry method for real-time camera pose estimation. Finally, an adaptive monocular visual–inertial SLAM is implemented by presenting an adaptive execution module that dynamically selects visual–inertial odometry or optical-flow-based fast visual odometry. Experimental results show that the average translation root-mean-square error of keyframe trajectory is approximately 0.0617 m with the EuRoC dataset. The average tracking time is reduced by 7.8%, 12.9%, and 18.8% when different level-set adaptive policies are applied. Moreover, we conducted experiments with real mobile device sensors, and the results demonstrate the effectiveness of performance improvement using the proposed method.
Highlights
The rapid development of mobile devices such as unmanned aerial vehicles, handhold mobile devices, and augmented reality (AR)/virtual reality (VR) headsets has provided a good platform for AR technology
We propose an adaptive monocular visual–inertial Simultaneous localization and mapping (SLAM) for real-time AR
We evaluate the proposed adaptive visual–inertial SLAM system focusing on two main goals
Summary
The rapid development of mobile devices such as unmanned aerial vehicles, handhold mobile devices, and augmented reality (AR)/virtual reality (VR) headsets has provided a good platform for AR technology. SLAM is a low-level technology that provides map and location information to applications using it. The requirements for the map and location accuracy are different. If the application target is a robot that performs navigation tasks based on SLAM, it requires the entire map information and perceptible information about obstacles in the surrounding space. This results in a higher demand for SLAM mapping. In AR applications, the real-time camera pose and the distance between the camera and the object are more important, and the accuracy of SLAM system mapping and global positioning is relatively low
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