Lidar is an important sensor of the autonomous driving system to detect environmental obstacles, but the spatial distribution of its point cloud is non-uniform because of the scanning mechanism. For adaption to this spatial non-uniformity, a dynamic clustering algorithm is proposed based on the spatial distribution analysis of the point cloud along different directions. The proposed algorithm adopts an elliptical function to describe the neighbor, whose semi-minor and semi-major are adjusted dynamically according to the position of the core point. Base on the relationship analysis of different clustering parameters, they are further designed quantitatively by KITTI dataset considering comprehensive clustering performances. To validate the effectiveness of the proposed algorithm, several comparative experiments with different clustering methods and projection planes have been conducted in the campus by an electric sedan equipped with three IBEO LUX 8 lidars. The experimental results show that the proposed elliptical neighbor can deal with the uneven point cloud more effectively, the performances of over-segmentation, under- segmentation and missed detection all are improved and accordingly a higher detection accuracy is achieved.

中文翻译：

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自动驾驶系统激光雷达障碍物检测的动态聚类算法


激光雷达是自动驾驶系统检测环境障碍物的重要传感器，但由于扫描机制的原因，其点云的空间分布不均匀。为了适应这种空间不均匀性，基于点云沿不同方向的空间分布分析，提出了一种动态聚类算法。该算法采用椭圆函数来描述邻居，其半次和半主要根据核心点的位置动态调整。在分析不同聚类参数关系的基础上，考虑综合聚类性能，利用KITTI数据集进一步定量设计。为了验证所提算法的有效性，在校园内使用配备三台 IBEO LUX 8 激光雷达的电动轿车进行了多次不同聚类方法和投影平面的对比实验。实验结果表明，所提出的椭圆邻域能够更有效地处理不均匀的点云，过分割、欠分割和漏检的性能均得到改善，从而获得更高的检测精度。