Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization
Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization
复制标题
Sparse-3D 激光雷达户外基于地图的自动驾驶车辆定位
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. H. Adiwahono
中科院分区：
文献类型：
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作者：
Syed Zeeshan Ahmed;V. B. Saputra;Saurab Verma;Kun Zhang;A. H. Adiwahono
在室外环境中捕捉独特结构的困难阻碍了基于地图的自主车辆(AV)的定位性能。因此，这就需要使用高分辨率传感器来从环境中获取更多信息。然而，这种方法成本高昂，并限制了AV的大规模部署。为了克服这一缺陷，本文提出了一种基于室外地图的城市环境下基于稀疏三维激光雷达扫描数据的自主车辆定位方法。在该方法中，正态分布变换(NDT)方法的点到分布(P2D)公式被应用于蒙特卡罗局部化(MCL)框架中。该公式考虑了单个激光雷达点的测量，改进了定位的测量模型。此外，为了将定位应用于可扩展的室外环境，实现了灵活高效的地图结构。实验结果表明，该方法在室外视听环境下，特别是在稀疏激光雷达数据有限的情况下，显著提高了定位精度和鲁棒性。
Difficulties in capturing unique structures in the outdoor environment hinders the map-based 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 map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (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.