OBD-II Sensor Approaches for The IMU and GPS Based Apron Vehicle Positioning System

This research paper presents the extension of our previous multi-sensor fusion algorithm in order to provide a robust apron vehicle positioning system. Our work showed that the proposed sensor fusion strategy able to accommodate both Inertia Measurement Unit (IMU) and Global Positioning System (GPS) sensors to deliver better position estimation with low error rates. However, higher error tolerance in both sensors remains gaps for improvement. For example, the IMU-Accelerometer drifting effects. By introducing an On-Board Diagnostic-II (OBD-II) sensor to the system, a new parameter which is velocity from the vehicle can be obtained. This velocity parameter is exclusive since it gives an absolute zero value as the vehicle stops. On this paper, we introduce how to update our graded Kalman filter (GKF) algorithm with the new additional OBD-II sensor. Further, we analyze how the new obtained parameter could improve the whole system performances. Our experiment shows that absolute zero velocity feature from OBD-II sensor able to reduce the drifting effect especially when the vehicle is idle (non-moving condition). Moreover, based on the RMSE calculation, the addition of OBD-II sensor is able to reduce the error level of the system.