Localization and Pose Estimation

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.

Functions

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ahrsfilterOrientation from accelerometer, gyroscope, and magnetometer readings
ahrs10filterHeight and orientation from MARG and altimeter readings
complementaryFilterOrientation estimation from a complementary filter
ecompassOrientation from magnetometer and accelerometer readings
imufilterOrientation from accelerometer and gyroscope readings
insfilterCreate inertial navigation filter
insfilterAsyncEstimate pose from asynchronous MARG and GPS data
insfilterErrorStateEstimate pose from IMU, GPS, and monocular visual odometry (MVO) data
insfilterMARGEstimate pose from MARG and GPS data
insfilterNonholonomicEstimate pose with nonholonomic constraints
stateEstimatorPFCreate particle filter state estimator
getStateEstimateExtract best state estimate and covariance from particles
predictPredict state of robot in next time step
correctAdjust state estimate based on sensor measurement
matchScansEstimate pose between two laser scans
matchScansGridEstimate pose between two lidar scans using grid-based search
matchScansLineEstimate pose between two laser scans using line features
transformScanTransform laser scan based on relative pose
lidarScanCreate object for storing 2-D lidar scan
monteCarloLocalizationLocalize robot using range sensor data and map
lidarScanCreate object for storing 2-D lidar scan
getParticlesGet particles from localization algorithm
odometryMotionModelCreate an odometry motion model
likelihoodFieldSensorModelCreate a likelihood field range sensor model
navParticleResamplingPolicyCreate resampling policy object with resampling settings
poseGraph Create 2-D pose graph
poseGraph3D Create 3-D pose graph
addScanAdd scan to lidar SLAM map
addRelativePoseAdd relative pose to pose graph
optimizePoseGraphOptimize nodes in pose graph
removeLoopClosures Remove loop closures from pose graph
scansAndPoses Extract scans and corresponding poses

Topics

Sensor Fusion

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.

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.

Localization Algorithms

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.

Particle Filter Parameters

To use the stateEstimatorPF particle filter, you must specify parameters such as the number of particles, the initial particle location, and the state estimation method.

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.

Featured Examples

IMU and GPS Fusion for Inertial Navigation

IMU and GPS Fusion for Inertial Navigation

How you might build an IMU + GPS fusion algorithm suitable for unmanned aerial vehicles (UAVs) or quadcopters.

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Estimate Position and Orientation of a Ground Vehicle

Estimate Position and Orientation of a Ground Vehicle

Estimate the position and orientation of ground vehicles by fusing data from an inertial measurement unit (IMU) and a global positioning system (GPS) receiver.

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Estimate Orientation and Height Using IMU, Magnetometer, and Altimeter

Estimate Orientation and Height Using IMU, Magnetometer, and Altimeter

Fuse data from a 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer (together commonly referred to as a MARG sensor for Magnetic, Angular Rate, and Gravity), and 1-axis altimeter to estimate orientation and height.

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Visual-Inertial Odometry Using Synthetic Data

Visual-Inertial Odometry Using Synthetic Data

Estimate the pose (position and orientation) of a ground vehicle using an inertial measurement unit (IMU) and a monocular camera. In this example, you:

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Localize TurtleBot Using Monte Carlo Localization

Localize TurtleBot Using Monte Carlo Localization

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

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Navigation Toolbox Documentation
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