State estimation for legged robots on unstable and slippery terrain

By Michael Blösch, Christian Gehring, Péter Fankhauser, Marco Hutter, M. Höpflinger, Roland Yves Siegwart. 2013

163 citations recorded by the source corpus.

This paper presents a state estimation approach for legged robots based on stochastic filtering. The key idea is to extract information from the kinematic constraints given through the intermittent contacts with the ground and to fuse this information with inertial measurements. To this end, we design an unscented Kalman filter based on a consistent formulation of the underlying stochastic model. To increase the robustness of the filter, an outliers rejection methodology is included into the update step. Furthermore, we present the nonlinear observability analysis of the system, where, by considering the special nature of 3D rotations, we obtain a relatively simple form of the corresponding observability matrix. This yields, that, except for the global position and the yaw angle, all states are in general observable. This also holds if only one foot is in contact with the ground. The presented filter is evaluated on a real quadruped robot trotting over an uneven and slippery terrain.

Subfields: Aerospace Engineering, Artificial Intelligence, Biomedical Engineering

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Citations in the corpus

Learning quadrupedal locomotion over challenging terrain (2020, background): “Other systems employ contact sensors at the feet, which can become unreliable in ﬁeld conditions [9–11].”
ANYmal - a highly mobile and dynamic quadrupedal robot (2016, unknown): “ANYmal features a purely proprioceptive state estimation based on fusion of IMU, leg kinematics, and ground contact measurements [23], [24].”
Dynamic trotting on slopes for quadrupedal robots (2015, methodology): “The data from these three types of sensors are fused through an extended Kalman filter [16] to estimate the pose and linear and angular velocities of the torso with respect to a world frame W (cf.”
Contact-aided invariant extended Kalman filtering for robot state estimation (2019, methodology): “Using the bias corrected IMU measurements, the individual terms of the new robot-centric system dynamics can be derived as (Bloesch et al., 2013)”
ANYmal - toward legged robots for harsh environments (2017, methodology): “For state-estimation, we are building upon the framework described in [27].”
Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots (2018, background): “5GHz, dual core 64-bit) together with state estimation [19].”
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback (2017, methodology): “2 has been used similarly in previous filtering frameworks (Bloesch et al., 2013; Li and Mourikis, 2013), we extend the methodology to 3D unit vectors on the 2-sphere S(2).”
ROBOT-CENTRIC ELEVATION MAPPING WITH UNCERTAINTY ESTIMATES (2014, background): “8 It was shown that the position and the rotation around the gravity axis (yaw angle) are in general unobservable and are therefore subject to drift.”
Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization (2018, background): “In previous work [3], we have shown that using proprioceptive sensing only, the position and yaw-rotation of a mobile robot remain unobservable.”
Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots (2017, methodology): “Later [10], the same group extended their work by replacing the EKF with an Unscented Kalman Filter (UKF) and redefining the formulation of the state vector to be robot-centric (i.”