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.

翻译：我们为不同跨度表面的机器人导航提出了一个视觉和惯性地形分类网(VINet),我们使用新的导航标签计划,对未知表面进行地形分类和概括。我们提议的认知方法和适应性时间安排控制框架可以根据地形导航特性作出预测,并导致对已知和未知表面进行更好的地形分类和导航控制。我们的VINet可以在已知地形的监督下,在准确度方面达到98.37 %, 与以往方法相比,在未知地形上提高8.51%的精确度。我们将VINet安装在移动跟踪机器人上,用于跟踪和导航不同地形,我们显示在RMSE方面比基线控制器改进了10.3%。</s>