Invariant Filtering for Legged Humanoid Locomotion on a Dynamic Rigid Surface

State estimation for legged locomotion over a dynamic rigid surface (DRS), which is a rigid surface moving in the world frame (e.g., ships, aircraft, and trains), remained an underexplored problem. This article introduces an invariant extended Kalman filter that estimates the robot's pose and velocity during DRS locomotion by using common sensors of legged robots [e.g., inertial measurement units (IMUs), joint encoders, and RDB-D camera]. A key feature of the filter lies in that it explicitly addresses the nonstationary surface-foot contact point and the hybrid robot behaviors. Another key feature is that in the absence of IMU biases, the filter satisfies the attractive group affine and invariant observation conditions, and is thus provably convergent for the deterministic continuous phases. The observability analysis is performed to reveal the effects of DRS movement on the state observability, and the convergence property of the hybrid deterministic filter system is examined for the observable state variables. Experiments of a Digit humanoid robot walking on a pitching treadmill validate the effectiveness of the proposed filter under large estimation errors and moderate DRS movement. The video of the experiments can be found at: https://youtu.be/ScQIBFUSKzo.

Automated summary

This article introduces a method for state estimation during legged locomotion over a dynamic rigid surface. The method uses common sensors of legged robots and utilizes an invariant extended Kalman filter to estimate the pose and velocity of the robot.