Recognizing traversable terrain from 3D point cloud data is critical, as it directly impacts the performance of autonomous navigation in off-road environments. However, existing segmentation algorithms often struggle with challenges related to changes in data distribution, environmental specificity, and sensor variations. Moreover, when encountering sunken areas, their performance is frequently compromised, and they may even fail to recognize them. To address these challenges, we introduce B-TMS, a novel approach that performs map-wise terrain modeling and segmentation by utilizing Bayesian generalized kernel (BGK) within the graph structure known as the tri-grid field (TGF). Our experiments encompass various data distributions, ranging from single scans to partial maps, utilizing both public datasets representing urban scenes and off-road environments, and our own dataset acquired from extremely bumpy terrains. Our results demonstrate notable contributions, particularly in terms of robustness to data distribution variations, adaptability to diverse environmental conditions, and resilience against the challenges associated with parameter changes.

翻译：从三维点云数据中识别可通行地形至关重要，因为它直接影响自动驾驶在越野环境中的性能。然而，现有的分割算法常常面临数据分布变化、环境特异性以及传感器差异等挑战。此外，当遇到凹陷区域时，其性能常常受到影响，甚至可能无法识别这些区域。为了解决这些挑战，我们提出了B-TMS，这是一种新颖的方法，它在被称为三网格场（TGF）的图结构中利用贝叶斯广义核（BGK）进行地图级地形建模与分割。我们的实验涵盖了从单次扫描到局部地图的各种数据分布，使用了代表城市场景和越野环境的公共数据集，以及我们从极其崎岖地形获取的自有数据集。我们的结果展示了显著的贡献，特别是在对数据分布变化的鲁棒性、对不同环境条件的适应性以及对参数变化相关挑战的抵御能力方面。