The presented research aims at developing a sensor fusion technique for navigational posture estimation for a skid-steered mobile robot vehicle in nursery tree plantations. RTK-GPS and Fiber Optic Gyroscope sensors were used for determining the position and orientation of the robot vehicle. An Extended Kalman Filter (EKF) was developed through making the use of the complementary error features of these sensors. A specially designed experimental platform was used to generate circular and linear reference trajectories for RTK-GPS calibration and error modeling. The RTK-GPS error was modeled by an auto-regression method and error states were incorporated into EKF design. The EKF with AR (2) model was implemented on straight line data to check the effectiveness of the developed algorithm. The mean error after incorporating AR (2) model with EKF reduced significantly with 2.63 cm and 0.37 cm in x and y direction, with standard deviation of 1.86 cm and 0.65 cm, respectively for line 1. For line 3 and 4, the mean measurement error in y direction was 9.17 cm and 0.10 cm, respectively. After filtering, the error in y direction reduced more than 98%. The filter was effective in reducing the mean errors of the system, in x and y direction for all the four lines. Further, it could also be stated that the errors were observed more in the direction of travel of the robot. When robot was navigated through the poles, the positioning accuracy of the system increased after filtering. The accuracy of the system can further be enhanced by fine tuning of system noise covariance matrices. Extended Kalman Filter, Robot Navigation, GPS, Fiber Optic Gyroscope