State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU

By Michael Blösch, Marco Hutter, M. Höpflinger, Stefan Leutenegger, Christian Gehring, C. Remy, Roland Yves Siegwart. 2012

346 citations recorded by the source corpus.

—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.

Subfields: Biomedical Engineering, Control and Systems Engineering, Molecular Biology

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Citations in the corpus

Learning quadrupedal locomotion over challenging terrain (2020, positive): “When the student controller is deployed, the quantities in ot are replaced with readings from the proprioceptive sensors, and the base velocity and orientation are provided by a state estimator (37).”
Probabilistic foot contact estimation by fusing information from dynamics and differential/forward kinematics (2016, background): “The state of the robot is represented by the generalized coordinate q and the generalized velocities ˙ q which can be obtained from a state estimation system that fuses information from an Inertial Measurement Unit (IMU) and joint encoders [7].”
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot (2015, methodology): “[11] for a quadruped that uses a prediction model driven by inertial measurements and creates lter corrections using foothold measurements.”
Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications (2013, methodology): “The estimations passed through a PID controller before the result is used to determine the movement of the quadrotor’s motor.”
MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot (2018, methodology): “The continuous time model is converted into discrete time with a 1ms time step using an identical procedure to [20].”
State estimation for legged robots on unstable and slippery terrain (2013, methodology): “If compared to the filter presented in [2] the RMS errors for the velocity estimates as well as for the roll and pitch angles are roughly halved for this experiment (for pitch there −0.”
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors (2017, methodology): “Extended Kalman filters (EKFs) are often used to fuse highfrequency inertial and contact measurements to provide accurate velocity, and orientation estimates that are useful for the stabilizing feedback controller [4], [11].”
Contact-aided invariant extended Kalman filtering for robot state estimation (2019, methodology): “Due to its low computational complexity and accurate performance, the EKF quickly became the de facto standard of nonlinear filtering for many mobile robot applications, including wheeled vehicles, drones, and legged robots [24, 70, 15].”
Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds (2017, neutral): “The state estimator is described in [39].”
ANYmal - a highly mobile and dynamic quadrupedal robot (2016, neutral): “ANYmal features a purely proprioceptive state estimation based on fusion of IMU, leg kinematics, and ground contact measurements [23], [24].”
Learning robust perceptive locomotion for quadrupedal robots in the wild (2022, background): “Foot slippage in snow can also cause large drift in the kinematic pose estimation (46), making the map even more inconsistent.”
Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots (2017, methodology): “[8] developed a state estimator that fuses leg kinematics and inertial information using an Extended Kalman Filter (EKF).”