Terrain Estimation for Quadruped Robots Based on Kalman Filter

When legged robots walk on the unstructured road, it is significant for quadruped robots to use adaptive motion control strategies by sensing the terrain geometry. This paper introduces a contact probability approach, which fuses gait sequences, knee joint torque size, and kinematics model to promote the accuracy of contact state detection, only using proprioceptive sensing technology without using vision or lidar perception information. By fusing the amplitude of the knee joint torque, Inertial Measurement Unit (IMU), and kinematics, the contact information can be estimated under a framework of Kalman Filtering (KF). Furthermore, an estimation model of ground attitude angles is proposed in this paper, which utilizes the contact state estimation information to construct a virtual contact state plane based on the least squares method. What’s more, the pitch angle and roll angle of the contact state ground plane can be separated from the math model of the virtual plane. Experiments with the Unitree Robotics Go1 EDU robot show the success of estimating the ground’s pitch angle and roll angle, as well as the detection algorithm of contact state while trotting on the slope.