Autonomous vehicles have been emerging over the past few years because of the sophisticated processing of data from different types of perception sensors, such as LiDAR, radar, and camera. Ground segmentation plays an important role in the sequence of data processing for environment perception tasks, as it can help to reduce the size of data to be processed and further decrease the overall computational time. However, the over-segmentation, under-segmentation, or slow-segmentation on non-flat surface usually occurs due to the characteristics of the 3D LIDAR data, such as occlusion in complex urban environment. To address these problems, in this paper, we proposed a probability occupancy grid-based approach for real-time ground segmentation by using a single LiDAR sensor. The effectiveness and robustness of our proposed method are evaluated and demonstrated by the real-time experiments that span different traffic scenarios from heavy traffic to light traffic.

中文翻译：

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基于概率占用网格的实时 LiDAR 地面分割方法

由于对来自不同类型感知传感器（例如 LiDAR、雷达和摄像头）的数据进行了复杂的处理，自动驾驶汽车在过去几年中不断涌现。地面分割在环境感知任务的数据处理序列中起着重要作用，因为它有助于减少要处理的数据大小并进一步减少整体计算时间。然而，由于 3D LIDAR 数据的特性，例如复杂的城市环境中的遮挡，通常会发生在非平坦表面上的过度分割、欠分割或慢分割。为了解决这些问题，在本文中，我们提出了一种使用单个 LiDAR 传感器进行实时地面分割的基于概率占用网格的方法。