reset

Reset visual SLAM object

Since R2025a

Syntax

reset(vslam)

Description

reset(vslam) resets all internal processes and clears all data in the object.

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,TrackFeatureRange=[30,120]);

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

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

    if hasNewKeyFrame(vslam)
        % Display 3-D map points and camera trajectory
        plot(vslam);
    end

    % Get current status of system
    status = checkStatus(vslam);
end 

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,addedFramesIdx] = poses(vslam);

% Reset the system
reset(vslam)

Compare the estimated camera trajectory with the ground truth to evaluate the accuracy.

% Load ground truth
gTruthData = load("orbslamGroundTruth.mat");
gTruth     = gTruthData.gTruth;

% Evaluate tracking accuracy
mtrics = compareTrajectories(camPoses, gTruth(addedFramesIdx), AlignmentType="similarity");
disp(['Absolute RMSE for key frame location (m): ', num2str(mtrics.AbsoluteRMSE(2))]);

Absolute RMSE for key frame location (m): 0.20043

% Plot the absolute translation error at each key frame
figure
ax = plot(mtrics, "absolute-translation");
view(ax, [2.70 -49.20]); 

Input Arguments

vslam — Visual SLAM object

monovslam object

Visual SLAM object, specified as a monovslam object.

Version History

Introduced in R2025a