Lidar Labeler

Label ground truth data in lidar point clouds

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Description

TheLidar Labelerapp enables you to label objects in a point cloud or a point cloud sequence. The app reads point cloud data from PLY, PCAP, LAS, LAZ, ROS and PCD files. Using the app, you can:

Define cuboid region of interest (ROI), line, voxel ROI labels, and scene labels. Use them to interactively label your ground truth data.
Define attributes for the labels and use them to provide further detail about the labels.
Use built-in algorithms for clustering, ground plane segmentation, automated labeling, and tracking.
Save label definitions, point cloud data, and ground truth data to a session file for future use.
Use theProjected Viewoption to view the labels in top, front and side views simultaneously.
Use theCamera Viewoption to create and reuse custom views of the point cloud data.
Use theAuto Alignoption to rotate and best fit the cuboid to the cluster.
Use thelidar.syncImageViewer.SyncImageViewerclass to sync the app to an external visualization or analysis tool.
Write, import, and use a custom automation algorithm for automated labeling.
Evaluate the performance of your label automation algorithms with a visual summary.
Export the labeled ground truth as agroundTruthLidarobject. This object can be used for system verification and training an object detector.

To learn more about this app, seeGet Started with the Lidar Labeler.

Open the Lidar Labeler App

MATLAB^®Toolstrip: On theAppstab, underImage Processing and Computer Vision, click the app icon.
MATLAB command prompt: EnterlidarLabeler.

Examples

Programmatic Use

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`lidarLabeler`

lidarLabeleropens a new session of the app, enabling you to label ground truth data in point clouds.

`lidarLabeler(velodyneLidarFileName,deviceModel,calibrationFile)`

lidarLabeler(velodyneLidarFileName,deviceModel,calibrationFile)opens the app and loads thevelodyneLidarFileName.

`lidarLabeler(ptCloudSeqFolder)`

lidarLabeler(ptCloudSeqFolder)opens the app and loads the point cloud sequence from the folderptCloudSeqFolder, whereptCloudSeqFolderis a string scalar or character vector specifying a folder that contains point cloud files. The point cloud files must have extensions supported bypcformats, and are loaded in the order returned by thedirfunction.

`lidarLabeler(lasSeqFolder)`

lidarLabeler(lasSeqFolder)opens the app and loads the LAS sequence from the folderlasSeqFolder, wherelasSeqFolderis a string scalar or character vector specifying a folder contains LAS files. LAS files must have extensions supported bylasformats, and are loaded in the order returned by thedirfunction.

`lidarLabeler(___,'SyncImageViewerTargetHandle',syncImageViewer)`

lidarLabeler(___,'SyncImageViewerTargetHandle',syncImageViewer)opens the app and loads both of these components:

A point cloud signal, specified using any of the input argument combinations from previous syntaxes.
An external video or image sequence display tool that is time-synchronized with the specified point cloud signal.

ThesyncImageViewerinput is a handle to alidar.syncImageViewer.SyncImageViewerclass that implements the external tool.

For example, this code opens the app with a point cloud signal and synchronized video visualization tool.

sourceName = fullfile(toolboxdir('lidar'),'lidardata','lcc',...'HDL64','pointCloud'); lidarLabeler(sourceName,'SyncImageViewerTargetHandle',@SyncImageDisplay)

`lidarLabeler(sessionFile)`

lidarLabeler(sessionFile)opens the app and loads a saved app sessionsessionFile. ThesessionFileinput contains the path and file name of a MAT-file. The MAT-file thatsessionFilepoints to contains the saved session.

`lidarLabeler(gTruth)`

lidarLabeler(gTruth)opens the app and loads agroundTruthobject .

Limitations

The labels do not support sublabels.
The Label Summary window does not support sublabels.

More About

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ROI Labels and Attributes

On the left side of the app, theROI Labelspane contains the ROI label definitions that you can mark on the point cloud frames. You can create label definitions directly from this pane. Alternatively, you can create label definitions programmatically by using alabelDefinitionCreatorLidarobject and then import these label definitions into an app session.

The app supports the definition of ROI labels and attributes.

ROI Labels

AnROI labelis a label that corresponds to an ROI in a signal frame. This table describes the supported label type.

ROI Label	Description	Example
`Cuboid`	Draw cuboidal ROI labels around objects.
`Line`	Draw line labels.
`Voxel`	Draw voxel ROI labels around objects.

You can show or hide the labels by using theicon on theROI Labelspane.

The只出现在你定义一个标签。默认情况下,the app displays all the labels. To hide a label, click on theicon alongside the label name. The app hides the corresponding label and displays theicon.

Show Label	Hide Label

ROI Attributes

AnROI attributespecifies additional information about an ROI label. For example, in a driving scene, attributes might include the type or color of a vehicle. This table describes the supported attribute types.

Attribute Type	Sample Attribute Definition	Sample Default Values
`Numeric Value`
`字符串`
`Logical`
`List`

Tips

Use thelidar.syncImageViewer.SyncImageViewerclass to create a tool for viewing the image corresponding to the point cloud data.
Remove the ground plane to clearly view the created object labels.
Use the rotate, translate, expand, and shrink options to edit the cuboids after drawing them.
Use theCamera Viewoption to save the a view of the data from the current angle and direction.
To avoid having to relabel ground truth with new labels, organize the labeling scheme you want to use before you begin marking your ground truth.
You can copy and paste the labels between signals that are of the same type.

Algorithms

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You can use label automation algorithms to speed up labeling within the app. To create your own label automation algorithm to use within the app, seeCreate Automation Algorithm for Labeling. You can also use one of the built-in algorithms by following these steps:

Import the data you want to label, and create at least one label definition.
On the app toolstrip, clickSelect Algorithmand select one of the built-in automation algorithms.
ClickAutomate, and then follow the automation instructions in the right pane of the automation window.

Lidar Object Tracker

Track an object through the point cloud frame. To use this algorithm, you must draw a cuboid ROI on an object you wish to track. You can also draw multiple cuboid ROIs to track more than one label. Running the algorithm provides tracking data of the labels that you can accept or reject. You can also undo the run and perform it again.

The step by step procedure is displayed on app when you select theLidar Object Trackeralgorithm.

Point Cloud Temporal Interpolator

Estimate cuboid ROIs between point cloud frames by interpolating the ROI locations across the time range. To use this algorithm, you must draw a cuboid ROI on a minimum of two frames: one at the beginning of the interval and one at the end of the interval. The interpolation algorithm estimates and draws ROIs in the intermediate frames.

Consider a point cloud sequence with 10 frames. The first frame has a cuboid ROI centered at [5, 5, 0]. The 10th frame has a cuboid ROI centered at [25, 25, 0]. At each frame, the algorithm moves the ROI 2 points in thex-direction, 2 points in they-direction, and 0 points in thez-direction. Therefore, the algorithm centers the ROI at [7, 7, 0] in the second frame, [9, 9, 0] in the third frame, and so on, up to [23, 23, 0] in the second-to-last frame.

版本历史

介绍了R2020b

Lidar Labeler

Description

Open the Lidar Labeler App

Examples

Programmatic Use

`lidarLabeler`

`lidarLabeler(velodyneLidarFileName,deviceModel,calibrationFile)`

`lidarLabeler(ptCloudSeqFolder)`

`lidarLabeler(lasSeqFolder)`

`lidarLabeler(___,'SyncImageViewerTargetHandle',syncImageViewer)`

`lidarLabeler(sessionFile)`

`lidarLabeler(gTruth)`

Limitations

More About

ROI Labels and Attributes

Tips

Algorithms

Lidar Object Tracker

Point Cloud Temporal Interpolator

版本历史

See Also

Apps

Objects

Classes