ABSTRACT
This chapter presents a unique nonlinear estimation strategy to simultaneously estimate the velocity and structure of a moving object using a single camera. It proposes an adaptive nonlinear estimator with the design of measurable and unmeasurable signal filters to reconstruct the range and the 3D Euclidean coordinates of feature points. The chapter also presents an adaptive nonlinear estimator to identify the range and the 3D Euclidean coordinates of feature points on an object under motion using a single camera. A Lyapunov-based analysis is then presented that indicates if a persistent excitation condition is satisfied then the range and the 3D Euclidean coordinates of each feature point can be determined. Lyapunov-based system analysis methods and homography-based vision techniques are used in the development and analysis of the identification algorithm. For simulations, the image acquisition hardware and the image processing step were both replaced with a software component that generated feature trajectories utilizing object kinematics.
