Towards Kinematics From Motion: Unknown Input Observer and Dynamic Extension Approach

This letter addresses an unknown input observer design to estimate simultaneously the 3D depth of a tracked image feature and camera linear velocity using low cost monocular inertial sensor. The kinematic model is at first, augmented via dynamic extension approach then described as quasi-Linear Parameter Varying (qLPV) model. Further, qLPV system transformed into Takagi-Sugeno (T-S) form with unmeasured premise variables. error convergence analysis performed based on Lyapunov theory Input State Stability (ISS) property ensure boundedness state estimation error. Gains that guarantee asymptotic stability can be properly computed by means Linear Matrix Inequalities (LMIs). Finally proposed validated synthetic data.