论文标题
RGB数据的户外机器人遍历性估算的域适应性具有安全性损失
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss
论文作者
论文摘要
能够估计移动机器人周围区域的遍历性是设计导航算法的基本任务。但是，这项任务通常很复杂，因为它需要评估与地形的障碍，类型和斜率的距离，并应对由于透视而导致的距离遥遥无期的非明显不连续性。在本文中，我们提出了一种基于深度学习的方法，以估算并预测板载RGB摄像机视野中不同路线的遍历得分。所提出的模型的主干基于最新的深层分割模型，该模型对预测路线穿术的任务进行了微调。然后，我们通过a）通过a）来提高模型的功能，通过梯度反转的无监督适应来解决域的转移，b）通过鼓励模型在安全方面犯错，即惩罚遇到障碍，而造成障碍物的碰撞，而不是会导致机器人提前停止的障碍。实验结果表明，我们的方法能够令人满意地识别可遍历的区域并概括到看不见的位置。
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.
