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Ground segmentation is an important preprocessing task for autonomousvehicles (AVs) with 3D LiDARs. To solve the problem of existing groundsegmentation methods being very difficult to balance accuracy and computationalcomplexity, a fast point cloud ground segmentation approach based on acoarse-to-fine Markov random field (MRF) method is proposed. The method uses animproved elevation map for ground coarse segmentation, and then usesspatiotemporal adjacent points to optimize the segmentation results. Theprocessed point cloud is classified into high-confidence obstacle points,ground points, and unknown classification points to initialize an MRF model.The graph cut method is then used to solve the model to achieve finesegmentation. Experiments on datasets showed that our method improves on otheralgorithms in terms of ground segmentation accuracy and is faster than othergraph-based algorithms, which require only a single core of an I7-3770 CPU toprocess a frame of Velodyne HDL-64E data (in 39.77 ms, on average). Field testswere also conducted to demonstrate the effectiveness of the proposed method.

A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field