English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

State Estimation for a Humanoid Robot

MPS-Authors
/persons/resource/persons85125

Righetti,  Ludovic
Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons76037

Schaal,  Stefan
Dept. Autonomous Motion, Max Planck Institute for Intelligent Systems, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Rotella, N., Bloesch, M., Righetti, L., & Schaal, S. (2014). State Estimation for a Humanoid Robot. In Proceedings of the IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS 2014) (pp. 952-958). IEEE. doi:10.1109/IROS.2014.6942674.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-D321-C
Abstract
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.