Improving localization accuracy for autonomous driving in snow-rain environments

Accurate localization is one of the most important issues for autonomous cars. This paper suggests a new LIDAR based method for improving the performance of localizing autonomous cars especially in snow-rain environment. Principal Component Analysis (PCA) is used to reconstruct LIDAR images in terms of enhancing quality and aligning pixel values to those in map images. In addition, edge-profile matching is incorporated to increase the accuracy of the lateral controlling in terms of reducing the effects of snow lines inside lanes. The real experimental results have verified that the proposed method is reliable and provides an acceptable localization error for driving autonomously in snow environments at maximum speed of 60 Km/h.