An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids

Ground segmentation is crucial for guiding mobile robots and identifying nearby objects. However, it should be noted that the ground often presents complex topographical features, such as slopes and rugged terrains, which significantly increase the challenges associated with accurate ground segmentation tasks. To address this issue, we propose a novel approach to achieve rapid ground segmentation. The proposed method uses a multi-partition approach to extract ground points for each partition, followed by assessing the correction plane based on geometric characteristics of the ground surface and similarity among adjacent planes. An adaptive threshold is also introduced to enhance efficiency in extracting complex urban pavement. Our method was benchmarked against several contemporary techniques on the SemanticKITTI dataset. The precision was elevated by 1.72 \(\%\) $\%$ , and the precision deviation was diminished by 1.02 \(\%\) $\%$ , culminating in the most accurate and robust outcomes among the evaluated methods.