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.

Funktionen

alle erweitern

Detect, Extract, and Match Features

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

Reconstruct 3-D Structure

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

Estimate Motion

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

Optimize Motion and 3-D Structure

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

Visualize Results

`imshow`	Display image
`showMatchedFeatures`	Display corresponding feature points
`plot`	Plot image view set views and connections (Seit 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 (Seit R2020a)
`worldpointset`	Manage 3-D to 2-D point correspondences (Seit R2020b)
`indexImages`	Create image search index
`invertedImageIndex`	Search index that maps visual words to images
`bagOfFeatures`	Bag of visual words object

Monocular Visual SLAM

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

RGB-D Visual SLAM

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

Stereo Visual SLAM

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

Themen

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.