vSLAM

Localisation et cartographie simultanées visuelles (vSLAM) monoculaires, stéréo et RGB-D

La localisation et la cartographie simultanées visuelles (vSLAM) consistent à calculer la position et l’orientation d’une caméra par rapport à son environnement tout en cartographiant ce dernier. Ce processus utilise uniquement les entrées visuelles de la caméra. Les applications du SLAM visuel sont notamment la réalité augmentée, la robotique et la conduite autonome. Pour plus de précisions, veuillez consulter Implement Visual SLAM in MATLAB.

Fonctions

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Détecter, extraire et faire correspondre des caractéristiques

`detectSURFFeatures`	Detect SURF features
`detectORBFeatures`	Detect ORB keypoints
`extractFeatures`	Extract interest point descriptors
`matchFeatures`	Find matching features
`matchFeaturesInRadius`	Find matching features within specified radius (depuis R2021a)

Reconstruire une structure 3D

`triangulate`	3-D locations of undistorted matching points in stereo images
`img2world2d`	Determine world coordinates of image points (depuis R2022b)
`world2img`	Project world points into image (depuis R2022b)

Estimer le mouvement

`estgeotform2d`	Estimate 2-D geometric transformation from matching point pairs (depuis R2022b)
`estgeotform3d`	Estimate 3-D geometric transformation from matching point pairs (depuis R2022b)
`estimateFundamentalMatrix`	Estimate fundamental matrix from corresponding points in stereo images
`estworldpose`	Estimate camera pose from 3-D to 2-D point correspondences (depuis R2022b)
`findWorldPointsInView`	Find world points observed in view (depuis R2020b)
`findWorldPointsInTracks`	Find world points that correspond to point tracks (depuis R2020b)
`estrelpose`	Calculate relative rotation and translation between camera poses (depuis R2022b)

Optimiser le mouvement et la structure 3D

`optimizePoses`	Optimize absolute poses using relative pose constraints (depuis R2020a)
`createPoseGraph`	Create pose graph (depuis R2020a)
`bundleAdjustment`	Adjust collection of 3-D points and camera poses
`bundleAdjustmentMotion`	Adjust collection of 3-D points and camera poses using motion-only bundle adjustment (depuis R2020a)
`bundleAdjustmentStructure`	Refine 3-D points using structure-only bundle adjustment (depuis R2020a)

Visualiser les résultats

`imshow`	Display image
`showMatchedFeatures`	Display corresponding feature points
`plot`	Plot image view set views and connections (depuis R2020a)
`plotCamera`	Plot a camera in 3-D coordinates
`pcshow`	Plot 3-D point cloud
`pcplayer`	Visualize streaming 3-D point cloud data

Gérer les données

`imageviewset`	Manage data for structure-from-motion, visual odometry, and visual SLAM (depuis R2020a)
`worldpointset`	Manage 3-D to 2-D point correspondences (depuis R2020b)
`indexImages`	Create image search index
`invertedImageIndex`	Search index that maps visual words to images
`bagOfFeatures`	Bag of visual words object

SLAM visuel monoculaire

`monovslam`	Visual simultaneous localization and mapping (vSLAM) with monocular camera (depuis R2023b)
`addFrame`	Add image frame to visual SLAM object (depuis R2023b)
`hasNewKeyFrame`	Check if new key frame added in visual SLAM object (depuis R2023b)
`checkStatus`	Check status of visual SLAM object (depuis R2023b)
`isDone`	End-of-file status (logical)
`mapPoints`	Build 3-D map of world points (depuis R2023b)
`poses`	Absolute camera poses of key frames (depuis R2023b)
`plot`	Plot 3-D map points and estimated camera trajectory in visual SLAM (depuis R2023b)
`reset`	Reset visual SLAM object (depuis R2023b)

SLAM visuel RGB-D

`rgbdvslam`	Feature-based visual simultaneous localization and mapping (vSLAM) with RGB-D camera (depuis R2024a)
`addFrame`	Add pair of color and depth images to RGB-D visual SLAM object (depuis R2024a)
`hasNewKeyFrame`	Check if new key frame added in RGB-D visual SLAM object (depuis R2024a)
`checkStatus`	Check status of visual RGB-D SLAM object (depuis R2024a)
`isDone`	End-of-processing status for RGB-D visual SLAM object (depuis R2024a)
`mapPoints`	Build 3-D map of world points from RGB-D vSLAM object (depuis R2024a)
`poses`	Absolute camera poses of RGB-D vSLAM key frames (depuis R2024a)
`plot`	Plot 3-D map points and estimated camera trajectory in RGB-D visual SLAM (depuis R2024a)
`reset`	Reset RGB-D visual SLAM object (depuis R2024a)

SLAM visuel stéréo

`stereovslam`	Feature-based visual simultaneous localization and mapping (vSLAM) with stereo camera (depuis R2024a)
`addFrame`	Add pair of color and depth images to stereo visual SLAM object (depuis R2024a)
`hasNewKeyFrame`	Check if new key frame added in stereo visual SLAM object (depuis R2024a)
`checkStatus`	Check status of stereo visual SLAM object (depuis R2024a)
`isDone`	End-of-processing status for stereo visual SLAM object (depuis R2024a)
`mapPoints`	Build 3-D map of world points from stereo vSLAM object (depuis R2024a)
`poses`	Absolute camera poses of stereo key frames (depuis R2024a)
`plot`	Plot 3-D map points and estimated camera trajectory in stereo visual SLAM (depuis R2024a)
`reset`	Reset stereo visual SLAM object (depuis R2024a)

Rubriques

Implement Visual SLAM in MATLAB
Understand the visual simultaneous localization and mapping (vSLAM) workflow and how to implement it using MATLAB.
Choose SLAM Workflow Based on Sensor Data
Choose the right simultaneous localization and mapping (SLAM) workflow and find topics, examples, and supported features.
Develop Visual SLAM Algorithm Using Unreal Engine Simulation (Automated Driving Toolbox)
Develop a visual simultaneous localization and mapping (SLAM) algorithm using image data from the Unreal Engine^® simulation environment.

Exemples présentés

Performant and Deployable Monocular Visual SLAM

Use visual inputs from a camera to perform vSLAM and generate multi-threaded C/C++ code.

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Monocular Visual Simultaneous Localization and Mapping

Visual simultaneous localization and mapping (vSLAM).

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Performant and Deployable Stereo Visual SLAM with Fisheye Images

Use fisheye image data from a stereo camera to perform VSLAM and generate multi-threaded C/C++ code.

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Stereo Visual Simultaneous Localization and Mapping

Process image data from a stereo camera to build a map of an outdoor environment and estimate the trajectory of the camera.

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Build and Deploy Visual SLAM Algorithm with ROS in MATLAB

Implement and generate C ++ code for a vSLAM algorithm that estimates poses for the TUM RGB-D Benchmark and deploy as an ROS node to a remote device.

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Visual Localization in a Parking Lot

Develop a visual localization system using synthetic image data from the Unreal Engine® simulation environment.

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Stereo Visual SLAM for UAV Navigation in 3D Simulation

Develop a visual SLAM algorithm for a UAV equipped with a stereo camera.

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Estimate Camera-to-IMU Transformation Using Extrinsic Calibration

Estimate SE(3) transformation to define spatial relationship between camera and IMU.

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