Nonlinear Filter Design for Pose and IMU Bias Estimation

Abstract — 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 con-current 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