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摘要:
针对从三维激光雷达点云中准确实时地分割地面的问题，提出一种基于扫描线段特征的地面分割算法。算法首先对三维点云进行去噪和位姿修正，接着依据相邻点间的欧氏距离和绝对高度差分割扫描线，然后对扫描线段的相邻线段间距、倾斜度、绝对高度差等特征进行分析，采用最大似然估计法求解特征阈值函数，提高了阈值的自适应性；最后综合考虑起伏、倾斜等复杂地形，通过制定横、纵向分类策略将扫描线标记为平坦地面线段、坡面线段和障碍物线段。本算法已成功应用在地面无人平台上，使用情况和对比试验表明，在城市和野外场景中，本算法都能够稳定高效地分割地面。
Abstract:Aiming at the problem of accurately segmenting the ground in real-time from 3D LiDAR point cloud, a ground segmentation algorithm based on the features of scanning line segments is proposed. The algorithm first performs de-noising and pose correction on the 3D point cloud, then divides the scanning line according to the Euclidean distance and absolute height difference between adjacent points. Next, the characteristics of the adjacent line segments such as spacing, slope, and absolute height difference are analyzed. The maximum likelihood estimation is used to solve the feature threshold function, which improves the adaptability of threshold. Finally, comprehensively considering the undulating and inclined complex terrain, the scanning line segments are marked as segments of flat ground, segments of slope and segments of obstacle by formulating the new horizontal and vertical classification strategies. This algorithm has been successfully applied to the unmanned ground platform. The usage and comparative test show that the algorithm can detect the ground stably and efficiently in both urban and field scenarios.
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Key words:
- 3D LiDAR /
- ground segmentation /
- segment features /
- complex terrain /
- real-time
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Overview: The development of unmanned vehicles is very rapid, but most of the studies are based on the urban environment, while the ground segmentation in the complex environment still faces many challenges. The problems include: 1) in the bumpy terrain, the platform will have changes in pitch, roll and suspension; 2) the LiDAR points are unevenly distributed, such as the measurement points in the area close to the LiDAR are densely distributed relatively, while the distribution of measurement points in the area away from the LiDAR is sparse, which results in a large range of gaps between different scanning lines; 3) in the case of processing a few millions of points, the accuracy and real-time of the segmentation are difficult to balance. This article conducts research aiming at the problem of accurately segmenting the ground in real-time from 3D point cloud in various environments. Considering that the existing methods are complex, long time consuming, or selected features are not universal, a ground segmentation algorithm based on the features of scanning line segments is proposed. The algorithm first performs de-noising and pose correction on the 3D point cloud, then divides the scanning line according to the Euclidean distance and absolute height difference between adjacent points. Next, the characteristics of the adjacent line segments such as spacing, slope, and absolute height difference are analyzed. The maximum likelihood estimation is used to solve the feature threshold function, which improves the adaptability of threshold. Finally, comprehensively considering the undulating and inclined complex terrain, combining the distribution characteristics of the features of scanning line segments, the scanning line segments are marked as segments of flat ground, segments of slope and segments of obstacle by formulating the new horizontal and vertical classification strategies: firstly select the line segment with the smallest height from the scanning line closest to the radar origin and mark it as the initial ground scanning line segment. Then determine the line segments type in the scanning line closest to the radar origin horizontally and determine the segments type in other scanning lines vertically. This algorithm has been successfully applied to the unmanned ground platform. The effect of the actual engineering application indicates that, the features selected in this paper have high sensitivity and easy extraction, which are less affected by noise than single point features. The segmentation algorithm is highly efficient and robust, which can detect the ground stably and efficiently in structured road scene, wild undulating road scene and complex undulating scene. And the comparative test results of the algorithm in this paper with the local elevation estimation algorithm in Ref. [13] and the feature fusion algorithm in Ref. [14] show that the segmentation effect of this algorithm is superior to the other two algorithms in accuracy and time-consuming.
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图 8 不同算法的分割耗时。(a)城市结构化道路场景；(b)野外颠簸道路场景；(c)复杂起伏场景
Figure 8. Elapsed time of different algorithms. (a) Urban structured road scene; (b) Wild bumpy road scene; (c) Complex undulating scene
表 1 不同场景中分割算法的效果对比
Table 1. Result comparison of segmentation algorithms in different scenes
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