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This paper introduces a new invariant extended Kalman filter design thatproduces real-time state estimates and rapid error convergence for theestimation of the human body movement even in the presence of sensormisalignment and initial state estimation errors. The filter fuses the datareturned 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 processmodel meets the group affine property while the filter explicitly addresses theIMU placement error by formulating its stochastic process model as Brownianmotions and incorporating the error in the leg odometry. Although themeasurement model is imperfect (i.e., it does not possess an invariantobservation 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 IMUplacement inaccuracy and initial estimation errors.

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