# Localization Algorithms

Localization algorithms, like Monte Carlo localization and scan matching, estimate your pose in a known map using range sensor or lidar readings. Pose graphs track your estimated poses and can be optimized based on edge constraints and loop closures. For simultaneous localization and mapping, see SLAM.

## Functions

## Topics

**Compose a Series of Laser Scans with Pose Changes**Use the

`matchScans`

function to compute the pose difference between a series of laser scans.**Minimize Search Range in Grid-based Lidar Scan Matching Using IMU**This example shows how to use an inertial measurement unit (IMU) to minimize the search range of the rotation angle for scan matching algorithms.

**Monte Carlo Localization Algorithm**The Monte Carlo Localization (MCL) algorithm is used to estimate the position and orientation of a robot.

**Particle Filter Workflow**A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.

**Particle Filter Parameters**To use the

`stateEstimatorPF`

(Robotics System Toolbox) particle filter, you must specify parameters such as the number of particles, the initial particle location, and the state estimation method.