Legged odometry based on fusion of leg kinematics and IMU information in a humanoid robot

Abstract

Position and velocity estimation are the key technologies to improve the motion control ability of humanoid robots. Aiming at solving the positioning problem of humanoid robots, we have designed a legged odometry algorithm based on forward kinematics and the feed back of IMU. We modeled the forward kinematics of the leg of the humanoid robot and used Kalman filter to fuse the kinematics information with IMU data, resulting in an accurate estimate of the humanoid robot’s position and velocity. This odometry method can be applied to different humanoid robots, requiring only that the robot is equipped with joint encoders and an IMU. It can also be extended to other legged robots. The effectiveness of the legged odometry scheme was demonstrated through simulations and physical tests conducted with the Walker2 humanoid robot.