Robust Keyframe Based Monocular SLAM for Augmented Reality
In this demo, we present RKSLAM, a robust keyframe based monocular SLAM system that can reliably handle fast motion with strong rotation and ensure good AR experiences. We contribute two key technical contributions: a novel multi homography based feature tracking method which is very robust and efficient, and a sliding window based camera pose optimization scheme which imposes the motion prior constraints between consecutive frames through simulated or real IMU data. Based on RKSLAM, we develop an AR App on a mobile device, which allows the user to freely insert 3D furniture models into the scene to see the AR effect without imagination.
2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR Adjunct)