Localization Algorithms

Particle filters, scan matching, Monte Carlo localization, pose graphs, odometry

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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Particle Filter State Estimation

`stateEstimatorPF`	Create particle filter state estimator
`resamplingPolicyPF`	Create resampling policy object with resampling settings

Scan Matching

`matchScans`	Estimate pose between two laser scans
`matchScansGrid`	Estimate pose between two lidar scans using grid-based search
`matchScansLine`	Estimate pose between two laser scans using line features (Since R2020a)
`transformScan`	Transform laser scan based on relative pose
`lidarScan`	Create object for storing 2-D lidar scan

Monte Carlo Localization

`monteCarloLocalization`	Localize robot using range sensor data and map
`lidarScan`	Create object for storing 2-D lidar scan
`odometryMotionModel`	Create an odometry motion model
`likelihoodFieldSensorModel`	Create likelihood field range sensor model

Pose Graphs

`poseGraph`	Create 2-D pose graph
`poseGraph3D`	Create 3-D pose graph
`poseGraphSolverOptions`	Solver options for pose graph optimization (Since R2020b)
`optimizePoseGraph`	Optimize nodes in pose graph
`trimLoopClosures`	Optimize pose graph and remove bad loop closures (Since R2020b)
`poseplot`	3-D pose plot (Since R2021b)

Factor Graph

`factorGraph`	Bipartite graph of factors and nodes (Since R2022a)
`importFactorGraph`	Import factor graph from g2o log file (Since R2022a)
`factorIMU`	Convert IMU readings to factor (Since R2022a)
`factorGPS`	Factor for GPS measurement (Since R2022a)
`factorTwoPoseSE2`	Factor relating two SE(2) poses (Since R2022a)
`factorTwoPoseSE3`	Factor relating two SE(3) poses (Since R2022a)
`factorPoseSE2AndPointXY`	Factor relating SE(2) position and 2-D point (Since R2022b)
`factorPoseSE3AndPointXYZ`	Factor relating SE(3) position and 3-D point (Since R2022b)
`factorIMUBiasPrior`	Prior factor for IMU bias (Since R2022a)
`factorVelocity3Prior`	Prior factor for 3-D velocity (Since R2022a)
`factorPoseSE3Prior`	Full-state prior factor for SE(3) pose (Since R2022a)
`factorCameraSE3AndPointXYZ`	Factor relating SE(3) camera pose and 3-D point (Since R2023a)
`estimateGravityRotation`	Estimate gravity rotation using IMU measurements and factor graph optimization (Since R2023a)
`estimateGravityRotationAndPoseScale`	Estimate gravity rotation and pose scale using IMU measurements and factor graph optimization (Since R2023a)
`estimateCameraIMUTransform`	Estimate transformation from camera to IMU sensor using calibration data (Since R2024a)

Wheel Encoder Odometry

`wheelEncoderOdometryAckermann`	Compute Ackermann vehicle odometry using wheel encoder ticks and steering angle (Since R2020b)
`wheelEncoderOdometryBicycle`	Compute bicycle odometry using wheel encoder ticks and steering angle (Since R2020b)
`wheelEncoderOdometryDifferentialDrive`	Compute differential-drive vehicle odometry using wheel encoder ticks (Since R2020b)
`wheelEncoderOdometryUnicycle`	Compute unicycle odometry using wheel encoder ticks and angular velocity (Since R2020b)

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.

Featured Examples

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

Implement monocular visual-inertial odometry to localize a UAV using camera and IMU data, optimized by a factor graph.

Since R2023b
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Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors

Estimate the position of a pedestrian using logged sensor data from an inertial measurement unit (IMU) and Global Positioning System (GPS) receiver and a factor graph.

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Map Indoor Area Using Lidar SLAM and Factor Graph

Implement offline lidar SLAM on collected lidar scans to map an indoor area using a factor graph and lidar scan map.

Open Live Script

Landmark SLAM Using AprilTag Markers

Combine robot odometry data and AprilTag markers to implement SLAM with a factor graph and pose graph, and compare the results.

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Localize TurtleBot Using Monte Carlo Localization Algorithm

Apply the Monte Carlo Localization algorithm on a TurtleBot® robot in a simulated Gazebo® environment.

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

Reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-D pose graph optimization. Visual odometry estimates the current global pose of the camera (current frame). Because of poor matching or errors in 3-D point triangulation, robot trajectories often tends to drift from the ground truth. Loop closure detection and pose graph optimization reduce this drift and correct for errors.

Open Live Script