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.

翻译：楼梯可通行区域的检测与物理建模是腿式机器人机动性的关键要素。本文提出一种专用于通过点云数据检测楼梯可通行区域并建模其物理属性的机载框架。为缓解光照变化影响及数据集多样性导致的过拟合，引入一系列数据增强方法以改进基础网络的训练。通过提出曲率抑制交叉熵损失函数，降低可通行与不可通行区域边界预测的模糊性。此外，基于楼梯位姿估计引入测量校正方法，以校准因倾斜视角影响的原始建模输出。最后，我们收集了楼梯相关数据集并提出新评估标准。通过在该数据集上开展的一系列严格实验，验证了所提方法具有优越的精度与泛化能力。代码、模型及数据集将发布于https://github.com/szturobotics/Stair-detection-and-modeling-project。