Linear Camera Velocities and Point Feature Depth Estimation Using Unknown Input Observer

Rayane Benyoucef (1) , Lamri Nehaoua (1) , Hicham Hadj-Abdelkader (1) , Hichem Arioui (1)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

In this paper, we propose a new approach to estimate the missing 3D information of a point feature during the camera motion and reconstruct the linear velocity of the camera. This approach is intended to solve the problem of relative localization and compute the distance between two Unmanned Aerial Vehicles (UAV) within a formation. An Unknown Input Observer is designed for the considered system described by a quasi-linear parameter varying (qLPV) model with unmeasurable variables to achieve kinematic from motion estimation. An observability analysis is performed to ensure the possibility of reconstructing the state variables. Sufficient conditions to design the observer are derived in terms of Linear Matrix Inequalities (LMIs) based on Lyapunov theory. Simulation results are discussed to validate the proposed approach.

Mots clés: Kinematic from Motion; LMI constraints; Feature Depth Estimation; Lyapunov Theory; qLPV Systems; Nonlinear Observers

Domaines: Automatique / Robotique

Rayane Benyoucef, Lamri Nehaoua, Hicham Hadj-Abdelkader, Hichem Arioui. Linear Camera Velocities and Point Feature Depth Estimation Using Unknown Input Observer. 11th IROS Workshop on Planning, Perception, Navigation for Intelligent part of Vehicle IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Nov 2019, Macau, China. ⟨hal-02417634⟩