  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.
