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

A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads