This paper deals with the development of a localization methodology for autonomous vehicles using only a $3\Dim$ LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is essentially to find its global $3\Dim$ pose (position and orientation) within this map. The problem of tracking is then to use sequential LIDAR scan measurement to also estimate other states such as velocity and angular rates, in addition to the global pose of the vehicle. Particle filters are often used in localization and tracking, as in applications of simultaneously localization and mapping. But particle filters become computationally prohibitive with the increase in particles, often required to localize in a large $3\Dim$ map. Further, computing the likelihood of a LIDAR scan for each particle is in itself a computationally expensive task, thus limiting the number of particles that can be used for real time performance. To this end, we propose a hybrid approach that combines the advantages of a particle filter with a global-local scan matching method to better inform the re-sampling stage of the particle filter. Further, we propose to use a pre-computed likelihood grid to speedup the computation of LIDAR scans. Finally, we develop the complete algorithm to extensively leverage parallel processing to achieve near sufficient real-time performance on publicly available KITTI datasets.
翻译:本文仅涉及为自主车辆开发本地化方法,仅使用3D美元LIDAR传感器。 在本文中,在已知的3D全球环境地图中将一台汽车本地化,基本上是为了发现其全球3D美元构成(位置和方向)在本地图中的位置(位置和方向)。随后,跟踪的问题是使用连续的LIDAR扫描测量方法来估计其他状态,例如速度和角速率,以及该车辆的全球面貌。粒子过滤器经常用于本地化和跟踪,如同时应用本地化和绘图。但粒子过滤器随着粒子的增加而变得计算性能过高,通常需要用3DIM美元大地图进行本地化。此外,计算每个粒子的LIDAR扫描可能性本身就是一个计算成本昂贵的任务,从而限制可用于实时性能的粒子数量。为此,我们提议一种混合方法,将粒子过滤器的优势与全球-本地扫描匹配方法结合起来,以更好地为粒子过滤器的完整复制阶段提供信息。此外,我们提议在接近的3Dimme时间上,我们提议使用一个可广泛进行实时扫描的电算法。