Real-time SLAM for Humanoid Robot Navigation Using Augmented Reality

The integration of Augmented Reality (AR) with Extended Kalman Filter based Simultaneously Localization and Mapping (EKF-SLAM) is proposed and implemented on a humanoid robot in this thesis. The goal has been to improve the performance of EKF-SLAM in terms of reducing the computational effort, to simplify the data association problem and to improve the trajectory control of the algorithm. Two applications of Augmented Reality are developed. In the first application, during a standard EKF-SLAM process, the humanoid robot recognizes specific and predefined graphical markers through its camera and obtains landmark information and navigation instruction using Augmented Reality. In the second stage, iPhone on-board gyroscope sensor is applied to achieve an enhanced positioning system, which is then used in conjunction of a PI motion controller for trajectory control. The proposed applications are implemented and verified in real-time on the humanoid robot NAO.