Main Content

Inertial navigation, pose estimation, scan matching, Monte Carlo
localization

Use localization and pose estimation algorithms to orient your vehicle in your environment. Sensor pose estimation uses filters to improve and combine sensor readings for IMU, GPS, and others. 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.

**Estimate Orientation Through Inertial Sensor Fusion**

This example shows how to use 6-axis and 9-axis fusion algorithms to compute orientation.

**Logged Sensor Data Alignment for Orientation Estimation**

This example shows how to align and preprocess logged sensor data.

**Lowpass Filter Orientation Using Quaternion SLERP**

This example shows how to use spherical linear interpolation (SLERP) to create sequences of quaternions and lowpass filter noisy trajectories.

**Pose Estimation From Asynchronous Sensors**

This example shows how you might fuse sensors at different rates to estimate pose.

**Choose Inertial Sensor Fusion Filters**

Applicability and Limitations of Inertial Sensor Fusion Filters.

**Estimate Orientation with a Complementary Filter and IMU Data**

This example shows how to stream IMU data from an Arduino and estimate orientation using a complementary filter.

**Estimating Orientation Using Inertial Sensor Fusion and MPU-9250**

This example shows how to get data from an InvenSense MPU-9250 IMU sensor and to use the 6-axis and 9-axis fusion algorithms in the sensor data to compute orientation of the device.

**Automatic Tuning of the insfilterAsync Filter**

The `insfilterAsync`

object is a complex extended Kalman filter that estimates the device pose.

**Localize TurtleBot Using Monte Carlo Localization**

This example demonstrates an application of the Monte Carlo Localization (MCL) algorithm on TurtleBot® in simulated Gazebo® environment.

**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.

**Reduce Drift in 3-D Visual Odometry Trajectory Using Pose Graphs**

This example shows how to reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-D pose graph optimization.

**Monte Carlo Localization Algorithm**

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

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.

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