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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.


Lidar ViewerVisualize and analyze lidar data


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
pc2demCreate digital elevation model (DEM) of point cloud data
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
detectRectangularPlanePointsDetect rectangular plane of specified dimensions in point cloud


Lidar Processing Overview

High-level overview of lidar applications.

Get Started with Lidar Viewer

Interactively visualize and analyze lidar data.

Estimate Transformation Between Two Point Clouds Using Features

This example shows how to estimate a rigid transformation between two point clouds.

What are Organized and Unorganized Point Clouds?

Define unorganized and organized point clouds and how to convert the former to latter.

Featured Examples