RACG-Ground: Ring-Adaptive Confidence Gating for LiDAR Ground Segmentation

Abstract

LiDAR has been widely used in autonomous driving and robotic navigation, while ground segmentation is a key preprocessing step. However, existing methods often suffer from over segmentation, under segmentation or insufficient real-time performance. In this letter, RACG-Ground, Ring-Adaptive Confidence Gating is developed for LiDAR Ground Segmentation, which is a fast and purely geometry-based ground segmentation approach. The method greedily aggregates fixed step radial cells into adaptive polar cells, which improves radial uniformity and reduces empty cells under range-dependent sparsity. On this grid, a cell-level confidence gating mechanism is designed in which vertical dispersion, upward offset, and lateral gradient are fused, with frame-wise adaptive thresholds employed to handle scene changes. A robust radial ground filter is then performed along each sector with lateral consensus, followed by point-wise multi- criterion gating that combines asymmetric tolerance bands and reference-height consistency for the final classification. Extensive experiments on SemanticKITTI (64 beams), nuScenes (32 beams), and SemanticTHAB (128 beams), together with real-world field tests show that RACG-Ground is accurate and real-time. On SemanticKITTI, it achieves 94.34% F1 at an average of 187 Hz, indicating robustness to outliers and occlusion-induced shadows.