Robust Keyframe-based Monocular SLAM for Augmented Reality

Abstract

Keyframe-based SLAM has achieved great success in terms of accuracy, efficiency and scalability. However, due to parallax requirement and delay of map expansion, traditional keyframe-based methods easily encounter the robustness problem in the challenging cases especially for fast motion with strong rotation. For AR applications in practice, these challenging cases are easily encountered, since a home user may not carefully move the camera to avoid potential problems. With the above motivation, in this paper, we present RKSLAM, a robust keyframe-based monocular SLAM system that can reliably handle fast motion and strong rotation, ensuring good AR experiences. First, we propose a novel multihomography based feature tracking method which is robust and efficient for fast motion and strong rotation. Based on it, we propose a real-time local map expansion scheme to triangulate the observed 3D points immediately without delay. A sliding-window based camera pose optimization framework is proposed, which imposes the motion prior constraints between consecutive frames through simulated or real IMU data. Qualitative and quantitative comparisons with the state-of-the-art methods, and an AR application on mobile devices demonstrate the effectiveness of the proposed approach.