张宁 FLAME: Feature-Likelihood Based Mapping and Localization for Autonomous Vehicles
链接：https://pan.baidu.com/s/1kEPxpMvfHWVq9Du3PkX0DQ
提取码：4snl
FLAME：自动驾驶汽车基于特征相似性的建图和定位
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.
准确的车辆定位无疑是自动驾驶导航最关键和最基本的任务。尽管基于密集3D点云的地图可以进行精确的定位，但是在城市规模的环境中使用时，它们会带来巨大的存储和传输负担。在本文中，我们提出了一种用于LiDAR地图的高度压缩表示，以及一种用于车载LiDAR扫描的高效且鲁棒的实时对齐算法。所提出的建图框架（我们称为特征似然获取地图仿真（FLAME））需要的空间不到原始3D点云地图的0.1％。本质上，FLAME通过特征似然函数模拟原始地图。 特别是，FLAME对平面，杆和路缘特征进行建模。 这三个要素类在车辆道路之间是长期稳定的，不同的且常见的。 通过特征检测从LiDAR扫描中提取多类特征点。 提出了一种新的基于多类的点到分布对齐方法，以找到多类特征点和FLAME图之间的关联和对齐。 实验结果表明，所提出的框架可以达到与基于3D点云的定位相同的精度水平（小于10cm）。