  The detection of traversable regions on staircases and the physical modeling
constitutes pivotal aspects of the mobility of legged robots. This paper
presents an onboard framework tailored to the detection of traversable regions
and the modeling of physical attributes of staircases by point cloud data. To
mitigate the influence of illumination variations and the overfitting due to
the dataset diversity, a series of data augmentations are introduced to enhance
the training of the fundamental network. A curvature suppression
cross-entropy(CSCE) loss is proposed to reduce the ambiguity of prediction on
the boundary between traversable and non-traversable regions. Moreover, a
measurement correction based on the pose estimation of stairs is introduced to
calibrate the output of raw modeling that is influenced by tilted perspectives.
Lastly, we collect a dataset pertaining to staircases and introduce new
evaluation criteria. Through a series of rigorous experiments conducted on this
dataset, we substantiate the superior accuracy and generalization capabilities
of our proposed method. Codes, models, and datasets will be available at
https://github.com/szturobotics/Stair-detection-and-modeling-project.
