Maximum likelihood pose estimation using machine vision

The existing pose estimation algorithms do not make any statistical assumptions on the sampled data, and lack the evaluation criteria. Aiming at this problem, based on the probability density function of the signal, we derived the general form of maximum likelihood pose estimation based on machine vision was and proved that the traditional iterative algorithm is equivalent to the maximum likelihood estimation using single imag ein the case of isotropic Gaussian noise.What's more, we derived the Cramér-Rao bound of pose estimation, which could be regarded as the variance low bound of any unbiased estimations. By the analysis of the simulation, the maximum likelihood method is much better than the traditional iterative method by using 10 pictures when noise power is greater than 5 dB, it proves that the performance of pose estimation can be improved by increasing the number of images.