Robust Keyframe based Monocular SLAM for Augmented Reality
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.
2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)