vSLAM

Monocular, stereo, and RGB-D visual simultaneous localization and mapping (vSLAM)

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. The process uses only visual inputs from the camera. Applications for visual SLAM include augmented reality, robotics, and autonomous driving. For more details, see Implement Visual SLAM in MATLAB.

Functions

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Detect, Extract, and Match Features

`detectSURFFeatures`	Detect SURF features
`detectORBFeatures`	Detect ORB keypoints (Since R2019a)
`extractFeatures`	Extract interest point descriptors
`matchFeatures`	Find matching features
`matchFeaturesInRadius`	Find matching features within specified radius (Since R2021a)

Reconstruct 3-D Structure

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

Estimate Motion

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

Optimize Motion and 3-D Structure

`optimizePoses`	Optimize absolute poses using relative pose constraints (Since R2020a)
`createPoseGraph`	Create pose graph (Since 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 (Since R2020a)
`bundleAdjustmentStructure`	Refine 3-D points using structure-only bundle adjustment (Since R2020a)

Visualize Results

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

Manage Data

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

monovslam

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

Topics

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.
Code Generation for Monocular Visual Simultaneous Localization and Mapping
Use MATLAB^® Coder™ to generate C++ code for the Monocular Visual SLAM example.
Visual Localization in a Parking Lot
Develop a visual localization system using synthetic image data from the Unreal Engine® simulation environment.
Stereo Visual SLAM for UAV Navigation in 3D Simulation
Develop a visual SLAM algorithm for a UAV equipped with a stereo camera.
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.
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.

Featured Examples

Monocular Visual Simultaneous Localization and Mapping

Visual simultaneous localization and mapping (vSLAM).

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Code Generation for Monocular Visual Simultaneous Localization and Mapping

Use the MATLAB® Coder™ to generate C/C++ code for the visual simultaneous localization and mapping algorithm from the Monocular Visual Simultaneous Localization and Mapping example.

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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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