From the last article, we get the following negative log likelihood function as our optimization target:

The optimization problem turns to be:

This article will explain how can this optimization problem be solved using Gauss-Newton method.

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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:

g2o_edge_vertex_noedge

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