Implementation of Multi-Sensor GPS/IMU Integration Using Kalman Filter for Autonomous Vehicle

Vehicle localization and position determination is a major factor for the operation of Autonomous Vehicle. Errors or unavailability of resources to determine this, poses a serious threat not only to the vehicle but also the environment around it. Global Positioning System (GPS) is one of the most common resources to determine position about the reference geographic coordinate system. But this resource has several drawbacks of its own viz. clock errors, multi-path errors and also uncertainty of good signal strength due to weather conditions or physical barriers. Also an additional drawback of a low-update rate makes it unreliable for the Autonomous Localization algorithm to operate on this. Thus a system is required which has no external environment dependencies to determine the position of the vehicle. Inertial Measurement Unit is a coupled system comprising of a 3-axis accelerometer and 3-axis gyroscope which records body force accelerations and the yaw rate. The sensor is loosely coupled with GPS system using Kalman Filter to predict and update vehicle position even at the event of loss of GPS signal. Extended research has been carried out in this discipline using different system architecture and methodologies. The novelty of this work lies in the simplicity and the methodology involved in combining two Kalman Filters, each for each sensor in a cascading manner resulting in the improvement of accuracy compared to a stand-alone GPS system.