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Algoritmi di localizzazione

Filtri di particelle, corrispondenza di scansione, localizzazione Monte Carlo, grafici di posa, odometria

Gli algoritmi di localizzazione, come la localizzazione Monte Carlo e lo scan matching, stimano la tua posizione in una mappa nota utilizzando letture di sensori di distanza o LiDAR. I grafici delle pose tengono traccia delle pose stimate e possono essere ottimizzati in base ai vincoli dei bordi e alle chiusure dei loop. Per la localizzazione e la mappatura simultanee, vedere SBATTERE .

Funzioni

espandi tutto

Stima dello stato del filtro antiparticolato

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

Corrispondenza della scansione

`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`	Crea oggetto per memorizzare la scansione LiDAR 2D

Localizzazione Monte Carlo

`monteCarloLocalization`	Localize robot using range sensor data and map
`lidarScan`	Crea oggetto per memorizzare la scansione LiDAR 2D
`odometryMotionModel`	Create an odometry motion model
`likelihoodFieldSensorModel`	Create likelihood field range sensor model

Grafici di posa

`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 (Da R2021b)

Grafico fattoriale

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

Odometria dell'encoder della ruota

`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

Argomenti

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.

Esempi in primo piano

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.

Da R2023b
Apri live script

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.

Apri live script

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.

Apri 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.

Apri live script

Localize TurtleBot Using Monte Carlo Localization Algorithm

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

Apri live script

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.

Apri live script