A New Method of 3D Point Cloud Data Processing in Low-speed Self-driving Car
A New Method of 3D Point Cloud Data Processing in Low-speed Self-driving Car
复制标题
低速自动驾驶汽车3D点云数据处理新方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Kangkang Xu
中科院分区：
文献类型：
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作者：
Xiaohua Wang;P. Ma;Lang Jiang;Li Li;Kangkang Xu
提出了一种新的点云数据处理方法。该方法专门用于基于激光雷达的低速自动驾驶汽车。点云数据处理的目的是减少三维原始点云的计算量和存储空间。同时，处理后的点云不同于普通的二维点云，不会丢失关键的空间信息点。该程序分为三个步骤。首先，选择有效的数据字段。然后利用统计离群点剔除算法对选定的数据字段进行过滤。其次，通过体素网格滤波器对滤波后的点云进行下采样。最后，将下采样的3D点云数据定期处理为2D数据。实验结果表明，该方法能较好地保留原始点云的关键空间信息点。处理后的点云需要更少的计算资源和存储空间，与普通的二维数据相比具有更高的可靠性，与三维数据相比降低了算法复杂度，并且能够进行真实的时间处理。实验车辆配备了velodyne-16传感器，可输出3D点云数据。作为实验的比较，车辆还包含输出普通2D数据的rplidar_A2传感器。
A novel method of point cloud data processing is proposed in this paper. This method is dedicated to the low-speed self-driving cars based on a lidar. The purpose of point cloud data processing is to reduce the amount of computation and its storage space of the 3D raw point cloud. Meanwhile, the processed point cloud is different from the ordinary 2D point cloud and it does not lose key spatial information points. The procedure is divided into three steps. Firstly, a valid data field is selected. And then the selected data field is filtered by the statistical outlier removal algorithm. Secondly, the point cloud filtered is downsampled by the voxel grid filter. Finally, the downsampled 3D point cloud data are processed to 2D data regularly. The experimental results show that the proposed method can retain the key spatial information points of the raw point cloud. The processed point cloud requires less computing resources and storage space, have higher reliability compared with the ordinary 2D data, reduce the algorithm complexity compared with 3D data, and be able to real time processing. The experimental vehicle equipped with a velodyne-16 sensor which outputs 3D point cloud data. As a comparison of the experiments, the vehicle also contains an rplidar_A2 sensor which outputs ordinary 2D data.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram