Localization of Autonomous Cars Using Multi-Sensor Data Fusion

This paper introduces a localization method where GPS, inertial measurement unit (IMU), odometer and 3D lidar are the main sensors. GPS is mainly used as a global position, and is also used as an initial position for map matching. The map matching is realized through the Normal Distributions transform (NDT)point cloud matching algorithm, by which the optimal transformation of the lidar point cloud and the established map is obtained. In case of GPS signal loss, the dead reckoning which is realized by the fusion of IMU and odometer is also applied. By comparing the experimental results to the accurate traj ectory, it is found that the positioning accuracy has been improved using the algorithm herein.