Robust LIDAR Localization for Autonomous Driving in Rain

This paper introduces a map-based localization method aiming to increase robustness in rainy conditions. This method utilizes two types of features: ground reflectivity features and vertical features extracted from 3D LIDAR scans and builds vehicle pose belief with two filters: a histogram filter and a particle filter. The posterior distributions from the two filters are integrated to estimate vehicle poses. This method exploits advantages of both features and filters, compensating respective weakness to deal with complex urban environments. Testing was performed in the fair and rainy weather. Road test results prove robustness and reliability of the proposed method.