Multi-sensor fusion for vehicle localization in real environment

It is essential for vehicles acquiring one's own current position to navigate autonomously. Especially, vehicles usually do not clearly obtain GPS information at indoor environments. In order to localize itself, vehicles have to use absolute and relative measurement systems from surrounding environmental and its driving information. Absolute position information such as environmental structures and magnets located on the ground can be used for offsetting the error of vehicle's relative position estimated from encoder and inertial sensors. In this paper, we describe how a variety of multi-sensors are integrated to measure the precise position of a vehicle. This means that as a way to reduce the relative position error, sensor fusion is able to produce accurate navigation information. In order to sense indoor environmental structures such as walls, artificial cones and magnets, we have employed LiDAR sensors and a magnet ruler, a device can detect magnetic field. Thus, we apply these information to Extended Kalman Filter for estimating vehicle's accurate position, and conduct two experiments to prove the effectiveness of the localization.