Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotion capabilities. We formulate a Kalman Filter (KF) for state estimation that integrates driven wheels into the filter equations and estimates the robot state (position and velocity) as well as the contribution of driving with wheels to the above state. Our estimation approach allows us to use the control framework of the Mini Cheetah quadruped robot with minor modifications. We tested our approach on this robot that we augmented with actively driven wheels in simulation and in the real world. The experimental results are available at https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq .

翻译：通过轮式四轮式机器人在平坦的地形上快速驾驶,可以实现快速和多功能的移动,但也能够通过调整身体姿势和步骤来克服具有挑战性的地形。在本文中,我们为四条腿的机器人提出了一个国家估计方法,这些机器人的轮子是不可移动的轮子,能够混合驾驶-踏脚移动能力。我们为国家估计设计了一个卡尔曼过滤器(KF),将轮子纳入过滤方程,并估计机器人状态(位置和速度)以及车轮驾驶对上述状态的贡献。我们的估计方法使我们能够使用小型Cheetah四轮式机器人的控制框架,并稍作修改。我们在模拟和现实世界中用积极驱动的轮子对这个机器人进行了测试。实验结果见https://www.ais.uni-bonn.de/%7Esseini/se-dsq。