Road DNA based localization for autonomous vehicles

High-precision and reliable localization is current research focus in the area of autonomous vehicles. Previous studies rely on either high-cost sensors or some specific characteristics, which means that the methods are limited to only a bit given situations. In this paper, a road DNA based localization method is proposed. It could afford high-precision result and does not have the shortcomings of previous methods at the same time. The scenery on both sides of the roads are used to generate the prior-map. The map is presented as grid map by the joint probability of occupation and reflectivity. With this type of map, different environments show different properties, which means that this method is not limited to specific environments and is effective in most cases. It costs much less memory than the previous maps. The map and live road scene flatting are both generated by data collected by low-cost LIDAR. Normalized Information Distance is utilized to align the live road scene flatting with the road DNA. Experiments show the validation and precision of this method.