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Nonlinear filter design for pose and IMU bias estimation

This paper concerns the problem of position and attitude estimation based on the fusion of complementary sensors. Nonlinear observers are proposed in order to obtain high-quality and high-rate estimates from raw position/attitude data, and measurements obtained from an inertial measurement unit. Estimates are improved through the online identification of diverse measurement biases, and a novel method for concurrent position estimation and accelerometer bias estimation is presented. Almost global asymptotic convergence of the pose observer is proved based on a Lyapunov-like approach, and several implementation issues are addressed. Comparison with classical existing methods illustrate the relevance of the proposed approach.