Robust Structure from Motion observer: Input to State Stability approach

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

1 IBISC - Informatique, BioInformatique, Systèmes Complexes

The authors present a novel nonlinear Thau-Luenberger observer for estimating Structure from Motion using a calibrated camera. Accurate reconstruction of the 3D structure of a scene relies on precise estimation of the camera’s translational and angular velocities, which can be challenging for cameras on mobile platforms. The proposed observer aims to estimate these velocities robustly in the presence of measurement noise, with stability characterized through Input to State Stability analysis and Lyapunov theory. The stability conditions are determined using optimization techniques based on Linear Matrix Inequalities. The performance of the proposed approach is validated through simulation and experimental data, demonstrating its effectiveness in recovering the depth of tracked features and its robustness against disturbances.

Keywords: Nonlinear Observer; Structure from Motion; Linear Matrix Inequalities

Domains: Automatic

Hichem Arioui, Lamri Nehaoua, Hicham Hadj-Abdelkader. Robust Structure from Motion observer: Input to State Stability approach. The 22nd World Congress of the International Federation of Automatic Control (IFAC 2023), Jul 2023, Yokohama, Japan. pp.11558--11563, ⟨10.1016/j.ifacol.2023.10.450⟩. ⟨hal-04068443⟩