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.

Keywords

Humanoid robots / State estimation / Legged odometry / Kalman filter

1 CRediT authorship contribution statement

Huailiang Ma: Writing - original draft, Validation, Investigation, Conceptualization. Aiguo Song: Conceptualization. Jingwei Li: Conceptualization. Ligang Ge: Resources. Chunjiang Fu: Resources. Guoteng Zhang: Writing - review & editing, Writing - original draft, Conceptualization.

2 Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

3 Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (62373223) and the Open Research Projects of the State Key Laboratory of Robotics (2023-010).

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