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SBATTERE

Localizzazione e mappatura simultanee 2D e 3D

La localizzazione e mappatura simultanea (SLAM) utilizza gli algoritmi Mappatura e per creare una mappa e localizzare contemporaneamente il veicolo su tale mappa. Utilizza lidarSLAM per mettere a punto il tuo algoritmo SLAM che elabora le scansioni lidar e le stime della posa dell'odometria per creare iterativamente una mappa. Utilizzare buildMap per acquisire dati registrati e filtrati per creare una mappa utilizzando SLAM. L'applicazione SLAM Map Builder consente di modificare manualmente le pose relative e di allineare le scansioni per migliorare la precisione della mappa.

Per maggiori informazioni su cosa sia SLAM e altri strumenti SLAM in altre toolboxMATLAB®, vedere Cos'è SLAM?.

App

SLAM Map BuilderCreare mappe a griglia 2D utilizzando SLAM basato su lidar

Funzioni

espandi tutto

monovslamVisual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with monocular camera (Da R2025a)
rgbdvslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with RGB-D camera (Da R2025a)
stereovslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with stereo camera (Da R2025a)
ekfSLAMPerform simultaneous localization and mapping using extended Kalman filter (Da R2021b)
lidarSLAM Perform localization and mapping using lidar scans
buildMapBuild occupancy map from lidar scans
factorGraphGraph-based framework for multisensor state estimation (Da R2022a)
importFactorGraphImport factor graph from g2o log file (Da R2022a)
factorIMUConvert IMU readings to factor (Da R2022a)
factorGPSFactor for GPS measurement (Da R2022a)
factorTwoPoseSE2Factor relating two SE(2) poses (Da R2022a)
factorTwoPoseSE3Relative pose factor connecting two SE(3) poses (Da R2022a)
factorTwoPoseSIM3Factor relating two SIM(3) poses (Da R2025a)
factorPoseSE2AndPointXYFactor relating SE(2) position and 2-D point (Da R2022b)
factorPoseSE3AndPointXYZFactor relating SE(3) position and 3-D point (Da R2022b)
factorIMUBiasPriorPrior factor for IMU bias (Da R2022a)
factorVelocity3PriorPrior factor for 3-D velocity (Da R2022a)
factorPoseSE3PriorFull-state prior factor for SE(3) pose (Da R2022a)
factorTransformSE3PriorFull-state prior factor for SE(3) transform (Da R2026a)
factorCameraSE3AndPointXYZFactor relating SE(3) camera pose, 3-D landmark point, and optional sensor transform (Da R2023a)
estimateGravityRotationEstimate gravity rotation using factor graph optimization (Da R2023a)
estimateGravityRotationAndPoseScaleEstimate gravity rotation and pose scale using factor graph optimization (Da R2023a)
estimateCameraIMUTransformEstimate transformation from camera to IMU sensor using calibration data (Da R2024a)
poseGraph Create 2-D pose graph
poseGraph3D Create 3-D pose graph
poseGraphSolverOptionsSolver options for pose graph optimization
optimizePoseGraphOptimize nodes in pose graph
trimLoopClosuresOptimize pose graph and remove bad loop closures
poseplot3-D pose plot (Da R2021b)

Argomenti

Esempi in primo piano

Performant and Deployable Monocular Visual SLAM

Performant and Deployable Monocular Visual SLAM

Visual simultaneous localization and mapping (vSLAM) refers to the process of calculating the position and orientation of a camera with respect to its surroundings while simultaneously mapping the environment. Applications for vSLAM include augmented reality, robotics, and autonomous driving. In this example, the algorithm uses only visual inputs from the camera.

Build a Map from Lidar Data Using SLAM

Build a Map from Lidar Data Using SLAM

Process 3-D lidar data from a sensor on a vehicle to progressively build a map and estimate the trajectory using SLAM.

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

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.

Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors

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.

Landmark SLAM Using AprilTag Markers

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.

Map Indoor Area Using Lidar SLAM and Factor Graph

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.

Build and Deploy Visual SLAM Algorithm with ROS in MATLAB

Build and Deploy Visual SLAM Algorithm with ROS in MATLAB

Implement a monocular visual SLAM algorithm to estimate camera poses and deploy generated C++ code using ROS.

Simulate RGB-D Visual SLAM System with Cosimulation in Gazebo and Simulink

Simulate RGB-D Visual SLAM System with Cosimulation in Gazebo and Simulink

Use RGB and depth images from a robot to simulate a RGB-D visual SLAM system in Simulink.

EKF-Based Landmark SLAM

EKF-Based Landmark SLAM

Implement landmark SLAM using the Extended Kalman Filter algorithm to track the path of a vehicle and map the surroundings.

Implementare la localizzazione e la mappatura simultanee (SLAM) con scansioni Lidar

Implementare la localizzazione e la mappatura simultanee (SLAM) con scansioni Lidar

Implementare SLAM offline utilizzando un grafico di posa e una serie di scansioni lidar e creare una mappa dell'ambiente.