Using a connected filter for structure estimation in perspective systems
Fredrik Nyberg, Ola Dahl, Jan Holst, Anders Heyden · 2007
Three-dimensional structure information can be esti-mated from two-dimensional images using recursive es-timation methods. This paper investigates possibilities to improve structure filter performance for a certain class of stochastic perspective systems by utilizing mutual in-formation, in particular when each observed point on a rigid object is affected by the same process noise. After presenting the dynamic system of interest, the method is applied, using an extended Kalman filter for the estimation, to a simulated time-varying multiple point vision system. The performance of a connected filter is compared, using Monte Carlo methods, to that of a set of independent filters. The idea is then further illustrated and analyzed by means of a simple linear system. Finally more formal stochas-tic differential equation aspects, especially the impact of transformations in the Ito ̂ sense, are discussed and related to physically realistic noise models in vision systems. 1.