A Fast Point Cloud Ground Segmentation Approach Based on Block-Sparsely Connected Coarse-to-Fine Markov Random Field

Ground segmentation is an essential preprocessing task for autonomous vehicles with 3D LiDARs. Nevertheless, current methods for ground segmentation fall short of achieving optimal performance, primarily hindered by under-segmentation, over-segmentation, slow-segmentation, and poor adaptability. This letter proposes a fast block-sparsely connected coarse-to-fine Markov Random Field (MRF) point cloud ground segmentation approach to address the above challenges. It starts with a ring-shaped elevation continuity map for non-ground segmentation, followed by a range image-based algorithm to separate high-confidence and uncertain points to complete the coarse segmentation. Finally, it uses a block-sparsely connected MRF construct method to organize the point cloud and employs the graph cut method to solve the MRFs for fine segmentation in parallel. Comparison with other state-of-the-art methods on the SemanticKITTI dataset demonstrates that the proposed algorithm achieves the highest accuracy among non-deep learning methods. Experiments on the 32-beam and 128-beam datasets demonstrate its advantages in terms of generalization capability. Additionally, our method processes Velodyne HDL-64E data frames in real-time (10.33 ms) on an Intel i9-11900 K CPU, which is significantly faster than other MRF-based methods.