Low-cost IMU and GPS fusion strategy for apron vehicle positioning

This paper presents a strategy to improve positioning estimation from low-cost Inertia Measurement Unit (IMU) sensor and Global Positioning System (GPS) for apron vehicle localization. IMU sensor provides raw acceleration values and its attitude, while GPS provides geodetic position, velocity, and heading course values. Fusion result from both sensors believed could comply Advanced-Surface Movement Guidance and Control System (A-SMGCS) standard with less economical cost. Within this paper, we propose graded Kalman filter method with several fusion steps. Our method consists of certain process filtering and process update which was associated one to each other. We also introduce a technique to handle the time synchronization and how to determine the low-cost sensor's error tolerance. Our preliminary experiment shows that proposed fusion strategy is able to accommodate both IMU and GPS sensors to provide better position estimation with lesser RMSE value in compared to the ground truth.