Accurate Kinematic-Parameters Estimation Using IMU and GPS Sensors Fusion

In this work, we developed a kinematic parameters estimator (KPE) using global positioning system (GPS) and inertial measurement unit (IMU) sensors embedded with a microcontroller. A Kalman filter is implemented in KPE to fuse IMU and GPS information. The filter estimates the short-range and long-rage positions simultaneously with the combination of the GPS data and IMU orientation information. We considered Kalman filter for sensor fusion which provides accurate position estimation despite of noise and drift. We have also performed the field trials to demonstrate the usability of the developed KPE. Evaluation of proposed solution through experiments in indoor and outdoor environments, demonstrating the position accuracy with few centimeters for short-range positioning and within a meter for long-range positioning.