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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment
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A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment

机译：基于多光束激光雷达的点云感知驾驶环境的框架

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摘要

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing.
机译：快速而准确地了解由路边，车辆，行人等组成的周围环境，对于开发智能车辆至关重要。这项工作中包含的道路要素是路边石和直接影响可驱动区域的动态道路障碍物。本文报道了一种使用多光束LIDAR的在线环境进行建模的框架，即Velodyne HDL-64E LIDAR，它以点云的形式描述了3D环境。首先，通过对Velodyne LIDAR捕获的原始数据进行多特征提取来进行地面分割，以满足在线环境建模的需求。路边和动态道路障碍物的检测和跟踪方式不同。曲线适用于路缘点，并且将点聚集成束，然后计算其形状和运动学参数。卡尔曼滤波器用于跟踪动态障碍物，而蛇形模型用于路缘石。结果表明，所提出的框架在各种环境下均很健壮，并且满足在线处理的要求。

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