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Lokalisierungsalgorithmen

Partikelfilter, Scan-Matching, Monte-Carlo-Lokalisierung, Pose-Graphen, Odometrie

Lokalisierungsalgorithmen wie Monte-Carlo-Lokalisierung und Scan-Matching schätzen Ihre Position auf einer bekannten Karte anhand von Entfernungssensor- oder Lidar-Messwerten. Pose-Graphen verfolgen Ihre geschätzten Posen und können basierend auf Randbeschränkungen und Schleifenschlüssen optimiert werden. Informationen zur gleichzeitigen Lokalisierung und Zuordnung finden Sie unter SLAM.

Funktionen

alle erweitern

Partikelfilter-Zustandsschätzung

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

Scan-Abgleich

`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
`transformScan`	Transform laser scan based on relative pose
`lidarScan`	Objekt zum Speichern von 2D-Lidar-Scans erstellen

Monte Carlo Lokalisierung

`monteCarloLocalization`	Localize robot using range sensor data and map
`lidarScan`	Objekt zum Speichern von 2D-Lidar-Scans erstellen
`odometryMotionModel`	Create an odometry motion model
`likelihoodFieldSensorModel`	Create likelihood field range sensor model

Posendiagramme

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

Faktordiagramm

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

Rad-Encoder-Odometrie

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

Themen

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.

Enthaltene Beispiele

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.

Seit 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.

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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.

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