checkStatus

Check status of visual SLAM object

Since R2023b

Syntax

status = checkStatus(vslam)

Description

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

Examples

Monocular Visual SLAM Using TUM RGB-D Data Set

Perform monocular visual simultaneous localization and mapping (vSLAM) using the data from the TUM RGB-D Benchmark. You can download the data to a temporary directory using a web browser or by running this code:

baseDownloadURL = "https://cvg.cit.tum.de/rgbd/dataset/freiburg3/rgbd_dataset_freiburg3_long_office_household.tgz"; 
dataFolder = fullfile(tempdir,"tum_rgbd_dataset",filesep); 
options = weboptions(Timeout=Inf);
tgzFileName = dataFolder+"fr3_office.tgz";
folderExists = exist(dataFolder,"dir");

% Create a folder in a temporary directory to save the downloaded file
if ~folderExists  
    mkdir(dataFolder) 
    disp("Downloading fr3_office.tgz (1.38 GB). This download can take a few minutes.") 
    websave(tgzFileName,baseDownloadURL,options); 
    
    % Extract contents of the downloaded file
    disp("Extracting fr3_office.tgz (1.38 GB) ...") 
    untar(tgzFileName,dataFolder); 
end

Create an imageDatastore object to store all the RGB images.

imageFolder = dataFolder+"rgbd_dataset_freiburg3_long_office_household/rgb/";
imds = imageDatastore(imageFolder);

Specify your camera intrinsic parameters, and use them to create a monocular visual SLAM object.

intrinsics = cameraIntrinsics([535.4 539.2],[320.1 247.6],[480 640]);
vslam = monovslam(intrinsics);

Process each image frame, and visualize the camera poses and 3-D map points.

for i = 1:numel(imds.Files)
    addFrame(vslam,readimage(imds,i))

    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);
end 

Note that the monovslam object runs several algorithm parts on separate threads, which can introduce a latency in processing of an image frame added by using the addFrame function.

% Plot intermediate results and wait until all images are processed
while ~isDone(vslam)
    if hasNewKeyFrame(vslam)
        plot(vslam);
    end
end

After all the images are processed, you can collect the final 3-D map points and camera poses for further analysis.

xyzPoints = mapPoints(vslam);
[camPoses,viewIds] = poses(vslam);

% Reset the system
reset(vslam)

Input Arguments

vslam — Visual SLAM object

monovslam object

Visual SLAM object, specified as a monovslam object.

Output Arguments

status — Current status of visual SLAM object

TrackingLost enumeration | TrackingSuccessful enumeration | FrequentKeyFrames enumeration

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

Enumeration Value	Numeric Value	Description
TrackingLost	uint8(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. This indicates the image does not contain enough features, or that the camera is moving too fast. To improve the tracking, you can increase the upperLimit value of the TrackFeatureRange argument and decrease the SkipMaxFrames argument to add key frames more frequently.
TrackingSuccessful	uint8(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.
FrequentKeyFrames	uint8(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.

Version History

Introduced in R2023b