State estimation for legged robots on unstable and slippery terrain

Abstract

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.

Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

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Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots

“…Control signals are generated in a 400Hz control loop which runs on the robot's on-board computer (Intel i7-7600U, 2.7 -3.5GHz, dual core 64-bit) together with state estimation [3]. For modeling and computation of kinematics and dynamics, we use the open-source Rigid Body Dynamics Library [15] (RBDL), which is a C++ implementation of the algorithms described in [8].…”

Collaborative navigation for flying and walking robots

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