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 letter presents a novel ground segmentation method called Patchwork, which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this letter, 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

中文翻译：

---

Patchwork：使用 3D LiDAR 传感器进行基于同心区域的区域地面分割和地面似然估计


地面分割对于地面移动平台执行导航或邻近物体识别至关重要。不幸的是，地面并不平坦，有陡峭的斜坡；崎岖不平的道路；或物体，例如路缘石、花坛等。为了解决这个问题，这封信提出了一种称为 Patchwork 的新颖地面分割方法，该方法对于解决分割不足问题非常稳健，并且运行频率超过 40 Hz。在这封信中，点云被编码为基于同心区域模型的表示，以计算不复杂的方式在箱之间分配适当的云点密度。接下来是区域地平面拟合，用于估计每个箱的部分地面。最后，引入地面似然估计以显着减少误报。正如在 SemanticKITTI 和粗糙地形数据集上的实验验证，我们提出的方法与最先进的方法相比具有良好的性能，与现有的基于平面拟合的方法相比显示出更快的速度。代码可用：https://github.com/LimHyungTae/patchwork