State estimation for legged robots: Consistent fusion of leg kinematics and IMU

Michael Bloesch, Marco Hutter, Mark A. Hoepflinger, Stefan Leutenegger, Christian Gehring, C. David Remy, Roland Siegwart

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

143 Scopus citations


This paper introduces a state estimation framework for legged robots that allows estimating the full pose of the robot without making any assumptions about the geometrical structure of its environment. This is achieved by means of an Observability Constrained Extended Kalman Filter that fuses kinematic encoder data with on-board IMU measurements. By including the absolute position of all footholds into the filter state, simple model equations can be formulated which accurately capture the uncertainties associated with the intermittent ground contacts. The resulting filter simultaneously estimates the position of all footholds and the pose of the main body. In the algorithmic formulation, special attention is paid to the consistency of the linearized filter: it maintains the same observability properties as the nonlinear system, which is a prerequisite for accurate state estimation. The presented approach is implemented in simulation and validated experimentally on an actual quadrupedal robot.

Original languageEnglish
Title of host publicationRobotics
Subtitle of host publicationScience and Systems VIII
EditorsNicholas Roy, Paul Newman, Siddhartha Srinivasa
PublisherMIT Press Journals
Number of pages8
ISBN (Print)9780262519687
StatePublished - 2013
Externally publishedYes
EventInternational Conference on Robotics Science and Systems, RSS 2012 - Sydney, Australia
Duration: 9 Jul 201213 Jul 2012

Publication series

NameRobotics: Science and Systems
ISSN (Print)2330-7668
ISSN (Electronic)2330-765X


ConferenceInternational Conference on Robotics Science and Systems, RSS 2012


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