State estimation for a humanoid robot

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

Authors

• Nicholas Rotella
• Michael Bloesch
• Ludovic Righetti
• Stefan Schaal

Keywords

• Foot
• Noise
• Quaternions
• Vectors
• State estimation
• Legged locomotion
• Humanoid Robot
• Kalman Filter
• Extended Kalman Filter
• Quadrupedal
• Flatfoot
• Singular Case
• Prediction Model
• Covariance Matrix
• Nonlinear Model
• Linear System
• Nonlinear Systems
• Angular Velocity
• Goal Of This Work
• Rotational Motion
• Inertial Measurement Unit
• Process Noise
• Pose Estimation
• Absolute Position
• Update Step
• Unscented Kalman Filter
• Ranking Loss
• Single Support
• Position Of The Foot
• Exponential Map
• Legged Robots
• Innovation Vector
• Noise Term
• Accelerometer
• Original Filter
• Robot Pose