poses
Description
[
returns the IDs camPoses
,keyFrameIDs
] = poses(vslam
)keyFrameIDs
of the key frames corresponding to the
camera poses. The IDs represent the order in which the image frames have been added to the
vslam
object by the addFrame
object
function.
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): 0.093645
% 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.
Output Arguments
camPoses
— Absolute camera poses of key frames
M-element array of rigidtform3d
objects
Absolute camera poses of key frames, returned as an M-element
array of rigidtform3d
objects.
keyFrameIDs
— Key frame IDs for absolute camera poses
M-element array of integers
Key frame IDs for the absolute camera poses, returned as an
M-element array of integers. Each element specifies the key frame ID
for the camera pose in the corresponding element of camPoses
. The
IDs represent the order in which the image frames have been added to the
vslam
object by the addFrame
object function.
Version History
Introduced in R2023b
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