论文标题
学习使用板载摄像头和稀疏的空中图像在非结构化环境中在光滑的地形上脱离道路
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
论文作者
论文摘要
我们提出了一种学习在平稳地形上行驶的方法，同时避免了仅使用视觉输入的挑战性越野和非结构化室外环境中的碰撞。我们的方法采用了一种基于混合模型和无模型的增强钢筋学习方法，该方法在使用板载传感器的地形粗糙度和碰撞标记中完全自我监督。值得注意的是，我们为模型提供第一人称和架空空中图像输入。我们发现，这些互补输入的融合可以改善计划的前景，并使模型可与视觉障碍物进行鲁棒性。我们的结果表明，有能力概括到具有丰富植被，各种类型的岩石和沙质小径的环境。在评估过程中，与仅使用第一人称图像的模型相比，我们的政策达到了90％的平滑地形遍历，并将较粗糙的地形的比例降低了6.1倍。
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We find that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6.1 times compared to a model using only first-person imagery.
