Purpose

Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate.

Design/methodology/approach

First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set.

Findings

The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy.

Research limitations/implications

Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study.

Originality/value

In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.

中文翻译：

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基于阶次和邻域点信息的移动激光雷达地面滤波算法

目的

现有的地面滤波算法大多基于笛卡尔坐标系，与移动光检测测距的工作原理不兼容，难以获得良好的滤波精度。本文的目的是通过充分利用球坐标中点与点之间的阶次信息来提高地面滤波的精度。

设计/方法/方法

首先，将布料模拟（CS）算法修改为分散点云的排序算法，得到点云的相邻关系，生成包含点云相邻信息的矩阵。然后，根据点的相邻信息，同时进行投影距离比较和局部斜率分析。综合这些结果以进一步处理点云细节，最终使用该算法从 KITTI 数据集中过滤场景中的点云。

发现

结果表明，KITTI点云排序的准确率为96.3%，地面滤波结果的kappa系数为0.7978。与应用于同一场景的其他算法相比，该算法具有更高的处理精度。

研究限制/影响

算法步骤为并行计算，计算量小，节省时间。此外，该算法的通用性得到了提高，可以用于城市街道的不同数据集。然而，由于缺乏带有标记地面点的野外环境点云，该算法在野外环境中的过滤结果需要进一步研究。

原创性/价值

在本研究中，点云相邻信息是通过改进的 CS 算法获得的。地面过滤算法根据点云数据中地面点的平坦度、连续性和极小度来区分地面点和离地点。另外，改变阈值对算法结果影响不大。