Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm

Abstract

Although several algorithms have been presented to solve the simultaneous pose and correspondence estimation problem, the correct solution may not be reached to with the traditional random-start initialization method. In this paper, we derive a novel method which estimates the initial value based on genetic algorithm, considering the influences of different initial guesses comprehensively. First, a set of random initial guesses is generated as candidate solutions. Second, the assignment matrix and the perspective projection error are computed for each candidate solution. And then each individual is modified (selection, crossover, and mutation) in current iterative process. Finally, the fittest individual is stochastically selected from the final population. With the presented initialization method, the proper initial guess could be first calculated and then the simultaneous pose and correspondence estimation problem could be solved easily. Simulation results with synthetic data and experiments on real images prove the effectiveness and robustness of our proposed method.