FLAME: FeatureLikelihood Based Mapping and Localization for Autonomous Vehicles Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D pointcloudbased maps enable precise localization, they impose significant storage and transmission burdens when used in cityscale environments. In this paper, we propose a highly compressed representation for LiDAR maps, along with an efficient and robust realtime alignment algorithm for onvehicle LiDAR scans. The proposed mapping framework, which we refer to as Feature Likelihood Acquisition Map Emulation (FLAME), requires less than 0.1% of the storage space of the original 3D point cloud map. In essence, FLAME emulates an original map through feature likelihood functions. In particular, FLAME models planar, pole and curb features. These three feature classes are longterm stable, distinct and common among vehicular roadways. Multiclass feature points are extracted from LiDAR scans through feature detection. A new multiclassbased pointtodistribution alignment method is proposed to find the association and alignment between the multiclass feature points and the FLAME map. The experimental results show that the proposed framework can achieve the same level of accuracy (less than 10cm) as the 3D point cloud based localization.

Sparse3D Lidar Outdoor MapBased Autonomous Vehicle Localization Difficulties in capturing unique structures in the outdoor environment hinders the mapbased Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor mapbased localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a PointtoDistribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.