Main Content

Preprocessing

Downsample, median filter, transform, extract features from, and align 3-D point clouds

Point cloud data from a lidar sensor has applications in robot navigation and perception, depth estimation, stereo vision, visual registration, and in advanced driver assistance systems (ADAS). Raw point cloud data from lidar sensors requires basic processing before utilizing it in these advanced workflows. Lidar Toolbox™ provides functionality for downsampling, median filtering, aligning, transforming, and extracting features from point clouds. These preliminary processing algorithms can improve the quality and accuracy of data, and obtain valuable information about the point clouds. This can be helpful in accelerating advanced workflows and provide better results.

Several advanced workflows require organized point clouds for processing. You can convert unorganized point clouds to organized point clouds with the Unorganized to Organized Conversion of Point Clouds Using Spherical Projection workflow.

Apps

Lidar ViewerVisualize and analyze lidar data

Functions

pcdownsampleDownsample a 3-D point cloud
pcmedianMedian filtering 3-D point cloud data
pcdenoiseRemove noise from 3-D point cloud
pcalignAlign an array point clouds
pccatConcatenate 3-D point cloud array
pcnormalsEstimate normals for point cloud
pctransformTransform 3-D point cloud
pcorganizeConvert 3-D point cloud into organized point cloud
lidarParametersLidar sensor parameters
lidarPointAttributesObject for storing lidar point attributes
pcregisterloamRegister two point clouds using LOAM algorithm
pc2demCreate digital elevation model (DEM) of point cloud data
pc2scanConvert 3-D point cloud into 2-D lidar scan
blockedPointCloudPoint cloud made from discrete blocks
blockedPointCloudDatastoreDatastore for use with blocks from blockedPointCloud objects
findNearestNeighborsFind nearest neighbors of a point in point cloud
findNeighborsInRadiusFind neighbors within a radius of a point in the point cloud
findPointsInROIFind points within a region of interest in the point cloud
removeInvalidPointsRemove invalid points from point cloud
extractEigenFeaturesExtract eigenvalue-based features from point cloud segments
extractFPFHFeaturesExtract fast point feature histogram (FPFH) descriptors from point cloud
detectISSFeaturesDetect ISS feature points in point cloud
detectLOAMFeaturesDetect LOAM feature points from 3-D lidar data
detectRectangularPlanePointsDetect rectangular plane of specified dimensions in point cloud

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