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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I/O and Storage

`pointCloud`	Object for storing 3-D point cloud
`velodyneFileReader`	Read point cloud data from Velodyne PCAP file
`pcread`	Read 3-D point cloud from PLY or PCD file
`pcwrite`	Write 3-D point cloud to PLY or PCD file

Preprocessing

`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

Visualization

`pcplayer`	Visualize streaming 3-D point cloud data
`pcshow`	Plot 3-D point cloud
`pcshowpair`	Visualize difference between two point clouds

Registration

`pcregistercpd`	Register two point clouds using CPD algorithm
`pcregistericp`	Register two point clouds using ICP algorithm
`pcregisterndt`	Register two point clouds using NDT algorithm

Segmentation

`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