Main Content

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

expand all

pointCloudObject for storing 3-D point cloud
velodyneFileReaderRead point cloud data from Velodyne PCAP file
pcreadRead 3-D point cloud from PLY or PCD file
pcwriteWrite 3-D point cloud to PLY or PCD file
pcdenoiseRemove noise from 3-D point cloud
pcdownsampleDownsample a 3-D point cloud
pcmergeMerge two 3-D point clouds
pcnormalsEstimate normals for point cloud
pctransformTransform 3-D point cloud
pcplayerVisualize streaming 3-D point cloud data
pcshowPlot 3-D point cloud
pcshowpairVisualize difference between two point clouds
pcregistercpdRegister two point clouds using CPD algorithm
pcregistericpRegister two point clouds using ICP algorithm
pcregisterndtRegister two point clouds using NDT algorithm
pcsegdistSegment point cloud into clusters based on Euclidean distance
segmentLidarDataSegment organized 3-D range data into clusters
segmentGroundFromLidarDataSegment ground points from organized lidar data
pcfitplaneFit plane to 3-D point cloud
planeModelObject for storing a parametric plane model

Topics

Detect, Classify, and Track Vehicles Using Lidar (Lidar Toolbox)

This example shows how to detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle.

Featured Examples