Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this paper presents a novel ground segmentation method called \textit{Patchwork}, which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this paper, a point cloud is encoded into a Concentric Zone Model-based representation to assign an appropriate density of cloud points among bins in a way that is not computationally complex. This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. Finally, Ground Likelihood Estimation is introduced to dramatically reduce false positives. As experimentally verified on SemanticKITTI and rough terrain datasets, our proposed method yields promising performance compared with the state-of-the-art methods, showing faster speed compared with existing plane fitting--based methods. Code is available: https://github.com/LimHyungTae/patchwork

翻译：地面分割对于地面移动平台进行导航或相邻天体识别至关重要。 不幸的是,地面不是平坦的,因为它具有陡峭的斜坡; 崎岖的道路; 或物体, 如路面、花床等等。 为了解决这个问题,本文件展示了一种新的地面分割方法,叫做\ textit{Patchwork}, 它对于解决分层不足问题非常有力,运行时间在40赫兹以上。 在本文中,点云被编码成一个基于Concentic Zone模型的表达方式,以不复杂的计算方式在垃圾桶中分配出适当的云点密度。 之后是区域地平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面。 http平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面,我们平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面。 : http httphttp:// http:// http:// http:// http://s: http:// http:// http:// : : http://g, et, et:: et, ts::::