Implementation of real-time positioning system using extended Kalman filter and artificial landmark on ceiling

Most localization algorithms use a range sensor or vision in a horizontal view, which usually imparts some disruption from a dynamic or static obstacle. By using landmarks on ceiling which the vehicle position were vertically measured, the disruption from horizontal view was reduced. We propose an indoor localization and navigation system based on an extended Kalman filter (EKF) and real-time vision system. A single upward facing digital camera was mounted on an autonomous vehicle as a vision sensor to recognize the landmarks. The landmarks consisted of multiple circles that were arranged in a defined pattern. Information on a landmark's direction and its identity as a reference for an autonomous vehicle was produced by the circular arrangements. The pattern of the circles was detected using a robust image processing algorithm. To reduce the noise that came from uneven light, the process of noise reduction was separated into several regions of interest. The accumulative error caused by odometry sensors (i.e., encoders and a gyro) and the vehicle's position were calculated and estimated, respectively, using the EKF algorithm. Both algorithms were tested on a vehicle in a real environment. The image processing method could precisely recognize the landmarks, and the EKF algorithm could accurately estimate the vehicle's position. The experimental results confirmed that the proposed approaches are implementable.