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：摘要：本文提出一种融合视觉与惯性信息的地形分类网络（VINet），用于机器人在不同可通行表面上的自主导航。我们采用基于导航特性的新型标签分配方案，实现未知地形分类与泛化。所提出的感知方法与自适应调度控制框架可根据地形导航属性进行预测，并在已知与未知表面上同时提升地形分类与导航控制的性能。在监督学习设定下，VINet对已知地形的分类准确率达98.37%，相比现有方法在未知地形上的准确率提升8.51%。我们将VINet部署于履带式移动机器人，实现多地形轨迹跟踪与导航，在均方根误差（RMSE）指标上较基准控制器降低10.3%。