Legged Robot State Estimation in Slippery Environments Using Invariant Extended Kalman Filter with Velocity Update

TL;DR

This paper introduces a novel state estimation method for legged robots in slippery environments using an Invariant Extended Kalman Filter that fuses inertial, velocity, and camera data, with auto-calibration and noise adaptation.

Contribution

It develops an InEKF-based estimator with auto-calibration of camera pose and online noise tuning, specifically designed for slippery terrain conditions.

Findings

The estimator accurately tracks robot states on slippery terrain.

The method demonstrates robustness to measurement noise and calibration errors.

Experimental validation confirms improved state estimation performance.

Abstract

This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints. {\color{black}The misalignment between the camera and the robot-frame is also modeled thus enabling auto-calibration of camera pose.} The leg kinematics based velocity measurement is formulated as a right-invariant observation. Nonlinear observability analysis shows that other than the rotation around the gravity vector and the absolute position, all states are observable except for some singular cases. Discrete observability analysis demonstrates that our filter is consistent with the underlying nonlinear system. An online noise parameter tuning method is developed to adapt to the highly time-varying camera measurement noise. The proposed method is experimentally validated on a Cassie bipedal robot walking over slippery terrain. A video for the experiment can be found at https://youtu.be/VIqJL0cUr7s.