  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.
