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Towards Kinematics from Motion : Unknown Input Observer & dynamic extension approach

Abstract : This paper addresses an unknown input observer design to estimate simultaneously the 3D depth of a tracked image feature and the camera linear velocity using a low cost monocular camera and inertial sensor. The camera kinematic model is at first, augmented via the dynamic extension approach then described as a quasi-Linear Parameter Varying (qLPV) model. Further, the qLPV system is transformed into TakagiSugeno (T-S) form with unmeasured premise variables. The error convergence analysis is performed based on Lyapunov theory and Input to State Stability (ISS) property to ensure the boundedness of the state estimation error. Gains that guarantee the asymptotic stability of the estimation error can be properly computed by means of Linear Matrix Inequalities (LMIs). Finally the proposed approach is validated using synthetic data.
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Submitted on : Friday, December 17, 2021 - 1:18:15 AM
Last modification on : Friday, April 15, 2022 - 11:08:03 AM
Long-term archiving on: : Friday, March 18, 2022 - 6:15:23 PM


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  • HAL Id : hal-03484322, version 1


Rayane Benyoucef, Hicham Hadj-Abdelkader, Lamri Nehaoua, Hichem Arioui. Towards Kinematics from Motion : Unknown Input Observer & dynamic extension approach. 60th IEEE conference on Decision and Control (CDC 2021), Dec 2021, Austin, Texas, United States. ⟨hal-03484322⟩



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