Gürkan Küçükyıldız, Suat Karakaya

In this study, a real-time indoor localization system was developed by using a camera and passive landmarks. A narrow band-pass infra-red (IR) filter was inserted to the back of the camera lens for capturing IR images. The passive landmarks were placed on the ceiling at pre-determined locations and consist of IR retro-reflective tags that have binary coded unique ID’s. An IR projector emits IR rays at the tags on the ceiling. The tags then reflect the rays back to the camera sensor creating a digital image. An image processing algorithm was developed to detect and decode the landmarks in captured images. The proposed algorithm successfully estimates the position and the orientation angle based on relative position and orientation with respect to the detected tags. To further improve the accuracy of the estimates, extended Kalman filter (EKF) was adapted to the measurement algorithm. The proposed method initially estimates the position of a mobile robot based on odometry and kinematic model. EKF was then used to update the estimates given the measurement obtained from the image processing system. Real time experiments were performed to test the performance of the system. The results prove that the proposed indoor localization system can effectively estimate position with an error less than 5cm.