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.

Funzioni

espandi tutto

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 (Da R2021a)

Reconstruct 3-D Structure

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

Estimate Motion

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

Optimize Motion and 3-D Structure

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

Visualize Results

`imshow`	Display image
`showMatchedFeatures`	Display corresponding feature points
`plot`	Plot image view set views and connections (Da 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 (Da R2020a)
`worldpointset`	Manage 3-D to 2-D point correspondences (Da 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 (Da R2023b)
`addFrame`	Add image frame to visual SLAM object (Da R2023b)
`hasNewKeyFrame`	Check if new key frame added in visual SLAM object (Da R2023b)
`checkStatus`	Check status of visual SLAM object (Da R2023b)
`isDone`	End-of-file status (logical)
`mapPoints`	Build 3-D map of world points (Da R2023b)
`poses`	Absolute camera poses of key frames (Da R2023b)
`plot`	Plot 3-D map points and estimated camera trajectory in visual SLAM (Da R2023b)
`reset`	Reset visual SLAM object (Da R2023b)

RGB-D Visual SLAM

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

Stereo Visual SLAM

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

Argomenti

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.