Ground segmentation, as the basic task of unmanned intelligent perception, provides an important support for the target detection task. Unstructured road scenes represented by open-pit mines have irregular boundary lines and uneven road surfaces, which lead to segmentation errors in current ground segmentation methods. To solve this problem, a ground segmentation method based on point cloud map is proposed, which involves three parts: region of interest extraction, point cloud registration and background subtraction. Firstly, establishing boundary semantic associations to obtain regions of interest in unstructured roads. Secondly, establishing the location association between point cloud map and the real-time point cloud of region of interest by semantics information. Thirdly, establishing a background model based on Gaussian distribution according to location association, and segments the ground in real-time point cloud by the background substraction method. Experimental results show that the correct segmentation rate of ground points is 99.95%, and the running time is 26ms. Compared with state of the art ground segmentation algorithm Patchwork++, the average accuracy of ground point segmentation is increased by 7.43%, and the running time is increased by 17ms. Furthermore, the proposed method is practically applied to unstructured road scenarios represented by open pit mines.

翻译：地面分割作为无人智能感知的基础任务，为目标检测任务提供了重要支撑。以露天矿为代表的非结构化道路场景存在边界线不规则、路面不平整等问题，导致现有地面分割方法出现分割误差。针对该问题，提出一种基于点云地图的地面分割方法，该方法包含感兴趣区域提取、点云配准和背景减除三个部分。首先，建立边界语义关联以获取非结构化道路的感兴趣区域。其次，通过语义信息建立点云地图与感兴趣区域实时点云之间的位置关联。再次，根据位置关联建立基于高斯分布的背景模型，并采用背景减除方法对实时点云中的地面进行分割。实验结果表明，地面点的正确分割率为99.95%，运行时间为26毫秒。与当前最先进的地面分割算法Patchwork++相比，地面点分割平均准确率提升了7.43%，运行时间提升了17毫秒。此外，该方法已实际应用于以露天矿为代表的非结构化道路场景。