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Depth Estimation for a Point Feature: Structure from motion & Stability Analysis

Abstract : This paper presents a new approach to recover the depth information from images of a monocular vision system. The depth's estimation for a point is achieved by designing a nonlinear observer based on a polytopic a structure. The fulfillment of the conditions of the state estimation, that depends on the applied velocities for the nonlinear system, is required. To this end, the observability analysis is performed to establish the kinematic conditions for the reconstruction of unmeasured states. The stability analysis is carried out using Lyapunuv theory. The observer gains were computed from the resolution of the Linear Matrix Inequality (LMI) constraints. Illustrations and simulation results are given at the end to prove the effectiveness of the proposed approach.
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https://hal.archives-ouvertes.fr/hal-02419275
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Submitted on : Thursday, December 19, 2019 - 1:22:43 PM
Last modification on : Monday, July 6, 2020 - 5:57:12 PM
Long-term archiving on: : Friday, March 20, 2020 - 7:01:57 PM

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Rayane Benyoucef, Hicham Hadj-Abdelkader, Lamri Nehaoua, Hichem Arioui. Depth Estimation for a Point Feature: Structure from motion & Stability Analysis. 58th IEEE Conference on Decision and Control (CDC 2019), Dec 2019, Nice, France. pp.3991--3996, ⟨10.1109/CDC40024.2019.9029396⟩. ⟨hal-02419275⟩

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