FLAME: Feature-Likelihood Based Mapping and Localization for Autonomous Vehicles

Abstract

Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D point-cloud-based maps enable precise localization, they impose significant storage and transmission burdens when used in city-scale environments. In this paper, we propose a highly compressed representation for LiDAR maps, along with an efficient and robust real-time alignment algorithm for on-vehicle 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 long-term stable, distinct and common among vehicular roadways. Multiclass feature points are extracted from LiDAR scans through feature detection. A new multiclass-based point-to-distribution 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.