Autonomous vehicle self-localization based on multilayer 2D vector map and multi-channel LiDAR

Abstract

Accurate vehicle self-localization is one of the crucial requirements of Autonomous vehicles. Since GPS-based localization techniques cannot achieve required accuracy in urban canyons, recently LiDAR-based (Light Detection and ranging) localization techniques gained a focus due to its accuracy. One of the challenges of LiDAR-based map matching methods is a size of the map. This paper proposes a new structure of map which is a multilayer 2D vector map and localization methods based on multi-channel LiDAR. Proposed map is extremely small in size comparing to 3D point cloud maps while preserving the localization accuracy. As this 2D map is generated by accumulating different layers of buildings, it has less uncertainty. Further, this map provides more features for map matching comparing to the conventional 2D maps and as a result, the accuracy of localization is improved. On the other hand, vector structure of the map bring more precise NDT (normal distribution transform) representation and as a result, more accurate matching. Experimental results show that proposed method outperform the conventional 2D map matching techniques in terms of accuracy.