Invariant Extended Kalman Filtering for Human Motion Estimation with Imperfect Sensor Placement

TL;DR

This paper presents a novel invariant extended Kalman filter for real-time human motion estimation that remains robust despite sensor misalignment and initial errors, leveraging group affine process models and leg odometry data.

Contribution

It introduces a new invariant EKF design that explicitly models IMU placement errors and achieves rapid convergence in human motion estimation tasks.

Findings

Fast convergence within 0.2 seconds during squatting motions

Robust performance despite significant IMU placement inaccuracies

Effective fusion of IMU data and leg odometry for accurate state estimation

Abstract

This paper introduces a new invariant extended Kalman filter design that produces real-time state estimates and rapid error convergence for the estimation of the human body movement even in the presence of sensor misalignment and initial state estimation errors. The filter fuses the data returned by an inertial measurement unit (IMU) attached to the body (e.g., pelvis or chest) and a virtual measurement of zero stance-foot velocity (i.e., leg odometry). The key novelty of the proposed filter lies in that its process model meets the group affine property while the filter explicitly addresses the IMU placement error by formulating its stochastic process model as Brownian motions and incorporating the error in the leg odometry. Although the measurement model is imperfect (i.e., it does not possess an invariant observation form) and thus its linearization relies on the state estimate, experimental results demonstrate fast convergence of the proposed filter (within 0.2 seconds) during squatting motions even under significant IMU placement inaccuracy and initial estimation errors.