Legged Robot State Estimation within Non-inertial Environments
arxiv(2024)
摘要
This paper investigates the robot state estimation problem within a
non-inertial environment. The proposed state estimation approach relaxes the
common assumption of static ground in the system modeling. The process and
measurement models explicitly treat the movement of the non-inertial
environments without requiring knowledge of its motion in the inertial frame or
relying on GPS or sensing environmental landmarks. Further, the proposed state
estimator is formulated as an invariant extended Kalman filter (InEKF) with the
deterministic part of its process model obeying the group-affine property,
leading to log-linear error dynamics. The observability analysis of the filter
confirms that the robot's pose (i.e., position and orientation) and velocity
relative to the non-inertial environment are observable. Hardware experiments
on a humanoid robot moving on a rotating and translating treadmill demonstrate
the high convergence rate and accuracy of the proposed InEKF even under
significant treadmill pitch sway, as well as large estimation errors.
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