Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontinuities in detected distances due to perspective. In this paper, we present an approach based on deep learning to estimate and anticipate the traversing score of different routes in the field of view of an on-board RGB camera. The backbone of the proposed model is based on a state-of-the-art deep segmentation model, which is fine-tuned on the task of predicting route traversability. We then enhance the model's capabilities by a) addressing domain shifts through gradient-reversal unsupervised adaptation, and b) accounting for the specific safety requirements of a mobile robot, by encouraging the model to err on the safe side, i.e., penalizing errors that would cause collisions with obstacles more than those that would cause the robot to stop in advance. Experimental results show that our approach is able to satisfactorily identify traversable areas and to generalize to unseen locations.

中文翻译：

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具有安全保护损失的 RGB 数据户外机器人可穿越性估计的域适应

能够估计移动机器人周围区域的可穿越性是导航算法设计中的一项基本任务。然而，该任务通常很复杂，因为它需要评估与障碍物的距离、地形的类型和坡度，并处理由于透视而导致的检测距离的非明显不连续性。在本文中，我们提出了一种基于深度学习的方法来估计和预测车载 RGB 相机视野中不同路线的穿越分数。所提出模型的主干基于最先进的深度分割模型，该模型针对预测路线可穿越性的任务进行了微调。然后，我们通过以下方式增强模型的能力： a) 通过梯度反转无监督适应解决域转移，b) 考虑到移动机器人的特定安全要求，通过鼓励模型在安全方面犯错，即惩罚会导致与障碍物碰撞的错误比导致机器人提前停止的错误更多。实验结果表明，我们的方法能够令人满意地识别可穿越区域并推广到看不见的位置。