Usually, all graph optimization papers or tutorials start from raising the optimization problem: minimizing the following function:
This article will explain where this optimization comes from and why it is related to gaussian noise.
Maximum Likelihood Estimation with Gaussian noise
The probabilistic modeling of graph optimization is based on the Maximum Likelihood Estimation(MLE) algorithm.
The case we use for this article will also be the one we used in the last article: