# Grid and Monte Carlo We will describe two localization algorithms that are capable of solving global localization problems. * They can process raw sensor measurements. There is no need to extract features from sensor values. * They are non-parametric. * They can solve global localization and kidnapped robot problems. The first approach is called *grid localization*. It uses a histogram filter to represent the posterior belief. The second approach is called the *Monte Carlo localization*, arguably the most popular localization algorithm to date. It uses particle filters to estimate posteriors over robot poses. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://calvinfeng.gitbook.io/probabilistic-robotics/localization/grid-and-monte-carlo.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.