论文标题
梅特集团：自动驾驶汽车的立即聚类激光雷达范围
InsClustering: Instantly Clustering LiDAR Range Measures for Autonomous Vehicle
论文作者
论文摘要
在距离测量中，激光雷达通常比摄像机更准确。因此，在自动驾驶中施加激光雷达有浓厚的兴趣。不同的现有方法处理丰富的3D点云，以进行对象检测，跟踪和识别。这些方法通常需要两个初始步骤：（1）地面平面上的滤波点，（2）将非地面点群集到对象中。本文为这两个步骤提出了一种经过现场测试的快速3D点云分割方法。我们专门设计的算法允许立即处理RAW LIDAR数据包，从而大大减少处理延迟。在我们对Velodyne Ultrapuck（32层旋转激光雷达）的测试中，聚类所有$ 360^\ Circ $ Lidar措施的处理延迟小于1ms。同时，采用粗到细节的方案来确保聚类质量。我们在公共道路上进行的现场实验表明，所提出的方法显着提高了3D点云聚集的速度，同时保持良好的准确性。
LiDARs are usually more accurate than cameras in distance measuring. Hence, there is strong interest to apply LiDARs in autonomous driving. Different existing approaches process the rich 3D point clouds for object detection, tracking and recognition. These methods generally require two initial steps: (1) filter points on the ground plane and (2) cluster non-ground points into objects. This paper proposes a field-tested fast 3D point cloud segmentation method for these two steps. Our specially designed algorithms allow instantly process raw LiDAR data packets, which significantly reduce the processing delay. In our tests on Velodyne UltraPuck, a 32 layers spinning LiDAR, the processing delay of clustering all the $360^\circ$ LiDAR measures is less than 1ms. Meanwhile, a coarse-to-fine scheme is applied to ensure the clustering quality. Our field experiments in public roads have shown that the proposed method significantly improves the speed of 3D point cloud clustering whilst maintains good accuracy.
