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checkStatus

Check status of stereo visual SLAM object

Since R2024a

    Description

    status = checkStatus(vslam) returns the current status of the stereo visual SLAM object. The frame the object is currently processing might be different than the most recently added frame.

    example

    Examples

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    Perform stereo visual simultaneous localization and mapping (vSLAM) using the data from the UTIAS Long-Term Localization and Mapping Dataset provided by University of Toronto Institute for Aerospace Studies. You can download the data to a directory using a web browser, or by running this code:

    ftpObj = ftp("asrl3.utias.utoronto.ca");
    tempFolder = fullfile(tempdir);
    dataFolder = [tempFolder,'2020-vtr-dataset\UTIAS-In-The-Dark\'];
    zipFileName = [dataFolder,'run_000005.zip'];
    folderExists = exist(dataFolder,"dir");

    Create a folder in a temporary directory to save the downloaded file and extract its contents.

    if ~folderExists  
        mkdir(dataFolder) 
        disp("Downloading run_000005.zip (818 MB). This download can take a few minutes.") 
        mget(ftpObj,"/2020-vtr-dataset/UTIAS-In-The-Dark/run_000005.zip",tempFolder);
    
        disp("Extracting run_000005.zip (818 MB) ...") 
        unzip(zipFileName,dataFolder); 
    end

    Create two imageDatastore objects to store the stereo images.

    imgFolderLeft = [dataFolder,'\images\left\'];
    imgFolderRight = [dataFolder,'\images\right\'];
    imdsLeft = imageDatastore(imgFolderLeft);
    imdsRight = imageDatastore(imgFolderRight);

    Specify the intrinsic parameters and the baseline of the stereo camera, and use them to create a stereo visual SLAM object. The focal length, principal point, and image size is in pixels, and the baseline is in meters.

    focalLength = [387.777 387.777];  
    principalPoint = [257.446 197.718];  
    imageSize = [384 512];            
    intrinsics = cameraIntrinsics(focalLength,principalPoint,imageSize);
    baseline = 0.239965; 
    
    vslam = stereovslam(intrinsics,baseline,MaxNumPoints=600, ...
        TrackFeatureRange=[30 120],SkipMaxFrames=5);

    Process each pair of stereo images and visualize the camera poses and 3-D map points.

    for i = 1:numel(imdsLeft.Files)
        leftImage = readimage(imdsLeft,i);
        rightImage = readimage(imdsRight,i);
        addFrame(vslam,leftImage,rightImage);
    
        if hasNewKeyFrame(vslam)
            % Query 3-D map points and camera poses
            xyzPoints = mapPoints(vslam);
            [camPoses,viewIds] = poses(vslam);
    
            % Display 3-D map points and camera trajectory
            plot(vslam);
        end
    
        % Get current status of system
        status = checkStatus(vslam);
        
        % Stop adding frames when tracking is lost
        if status == uint8(0)
            break
        end
    end 

    Once all the frames have been processed, reset the system.

    while ~isDone(vslam)
        plot(vslam)
    end

    Figure contains an axes object. The axes object with xlabel X, ylabel Y contains 12 objects of type line, text, patch, scatter. This object represents Camera trajectory.

    reset(vslam)

    Input Arguments

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    Stereo visual SLAM object, specified as a stereovslam object.

    Output Arguments

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    Current status of the stereo visual SLAM object, returned as a TrackingLost, TrackingSuccessful, or FrequentKeyFrames enumeration. This table describes these enumerations.

    Enumeration ValueNumeric ValueDescription
    TrackingLostuint8(0)

    Tracking is lost. The number of tracked feature points in the frame currently being processed is less than the lower limit of the TrackFeatureRange property of vslam. This indicates the image does not contain enough features, or that the camera is moving too fast.

    If the object does not accept enough frames as key frames, to improve tracking, you can increase the upperLimit value of the TrackFeatureRange property and decrease the SkipMaxFrames property to add key frames more frequently.

    TrackingSuccessfuluint8(1)

    Tracking is successful. The number of tracked feature points in the frame currently being processed is between the lower limit and upper limit values of the TrackFeatureRange property of vslam.

    FrequentKeyFramesuint8(2)

    Tracking adds key frames too frequently. The number of tracked feature points in the frame currently being processed is greater than the upper limit of the TrackFeatureRange property of vslam.

    Version History

    Introduced in R2024a

    See Also

    Objects

    Functions