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Lidar Processing

Velodyne®file import, segmentation, downsampling, transformations, visualization, and 3-D point cloud registration from lidar

Advanced driver assistance systems use 3-D point clouds obtained from lidar scans to measure physical surfaces. Lidar point cloud processing enables you to downsample, denoise, and transform these point clouds before registering them or segmenting them into clusters. You can also read, write, store, display, and compare point clouds, including point clouds imported from Velodyne packet capture (PCAP) files.

Functions

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pointCloud Object for storing 3-D point cloud
velodyneFileReader Read point cloud data fromVelodynePCAP file
pcread Read 3-D point cloud from PLY or PCD file
pcwrite Write 3-D point cloud to PLY or PCD file
ibeoFileReader Read message headers fromIbeoData Container (IDC) file
select Select subset of messages to read fromIbeoData Container (IDC) file
ibeoMessageReader Object for reading message content fromIbeoData Container (IDC) file
readMessages Read messages fromIbeoData Container (IDC) file selection
pcdenoise Remove noise from 3-D point cloud
pcdownsample Downsample a 3-D point cloud
pcmerge Merge two 3-D point clouds
pcnormals Estimate normals for point cloud
pctransform Transform 3-D point cloud
pcplayer Visualize streaming 3-D point cloud data
pcshow Plot 3-D point cloud
pcshowpair Visualize difference between two point clouds
pcregistercpd Register two point clouds using CPD algorithm
pcregistericp Register two point clouds using ICP algorithm
pcregisterndt Register two point clouds using NDT algorithm
pcregisterloam Register two point clouds using LOAM algorithm
pcsegdist Segment point cloud into clusters based on Euclidean distance
segmentLidarData Segment organized 3-D range data into clusters
segmentGroundFromLidarData Segment ground points from organized lidar data
pcfitplane Fit plane to 3-D point cloud
planeModel Object for storing a parametric plane model

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