论文标题
Vinet：在未知地形上的视觉和惯性地形分类和自适应导航
VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain
论文作者
论文摘要
我们提出了一个基于视觉和惯性的地形分类网络（VINET），用于在不同的遍布表面上进行机器人导航。我们使用一种新型的基于导航的标签方案来进行未知表面上的地形分类和概括。我们提出的感知方法和自适应调度控制框架可以根据地形导航属性进行预测，并在已知和未知表面上的地形分类和导航控制中提高性能。与以前的方法相比，我们的Vinet在已知地形的监督环境下的准确性可以达到98.37％，并在未知的地形上提高了8.51％的精度。我们在移动轨迹的机器人上部署Vinet，以进行轨迹，并在不同的地形上导航，并且与基线控制器相比，在RMSE方面，我们证明了10.3％的提高。
We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces. We use a novel navigation-based labeling scheme for terrain classification and generalization on unknown surfaces. Our proposed perception method and adaptive scheduling control framework can make predictions according to terrain navigation properties and lead to better performance on both terrain classification and navigation control on known and unknown surfaces. Our VINet can achieve 98.37% in terms of accuracy under supervised setting on known terrains and improve the accuracy by 8.51% on unknown terrains compared to previous methods. We deploy VINet on a mobile tracked robot for trajectory following and navigation on different terrains, and we demonstrate an improvement of 10.3% compared to a baseline controller in terms of RMSE.
