Preprocessing

Downsample, filter, transform, align, block, organize, and extract features from 3-D point cloud

Lidar sensors generate 3-D scans of their surrounding environments as collections of points in space called point clouds. Though point clouds are accurate and robust, which makes them useful for robotics applications, raw point cloud data is large, contains high density noise, and has a scattered distribution. Lidar Toolbox™ includes preprocessing features that enable you to better to store and use point clouds.

Lidar Toolbox includes preliminary processing algorithms to downsample, filter, transform, align, block, organize, and extract features from point clouds. These algorithms improve the quality and accuracy of the data, and can accelerate and improve the results of advanced workflows.
When your point cloud data is too large to process at once, you can divide and process the point cloud as small blocks by using the blockedPointCloud function.
For advanced workflows that require organized point clouds, such as object detection, and segmentation, you can convert unorganized point clouds to the organized format by using the pcorganize function. For more information on the distinctions between organized and unorganized point clouds, see What are Organized and Unorganized Point Clouds?
Lidar Toolbox includes functions that generate surface meshes, digital elevation models (DEM) and 2-D scans from point cloud data. You can also create and process surface mesh data by using the surfaceMesh object. Lidar Toolbox includes functions that read, write, and visualize a surface mesh.

You can also interactively visualize, analyze, and preprocess point cloud data using the Lidar Viewer app.

Apps

Lidar Viewer

Visualize and analyze lidar data

Functions

expand all

Preliminary Processing

`pcdownsample`	Downsample a 3-D point cloud
`pcmedian`	Median filtering 3-D point cloud data
`pcdenoise`	Remove noise from 3-D point cloud
`findPointsInCylinder`	Find points within a cylindrical region in a point cloud
`removeInvalidPoints`	Remove invalid points from point cloud
`removeHiddenPoints`	Remove hidden points from point cloud
`pcalign`	Align array of point clouds
`pccat`	Concatenate 3-D point cloud array
`pcnormals`	Estimate normals for point cloud
`pctransform`	Transform 3-D point cloud
`undistortEgoMotion`	Undistort point cloud affected by ego motion

Block Processing

`blockedPointCloud`	Point cloud made from discrete blocks
`blockedPointCloudDatastore`	Datastore for use with blocks from `blockedPointCloud` objects

Organize Point Cloud

pcorganize Convert 3-D point cloud into organized point cloud

Find Points in Point Cloud

`findNearestNeighbors`	Find nearest neighbors of a point in point cloud
`findNeighborsInRadius`	Find neighbors within a radius of a point in the point cloud
`findPointsInROI`	Find points within a region of interest in the point cloud

Detect and Extract Features

`extractEigenFeatures`	Extract eigenvalue-based features from point cloud segments
`extractFPFHFeatures`	Extract fast point feature histogram (FPFH) descriptors from point cloud
`detectISSFeatures`	Detect ISS feature points in point cloud
`detectLOAMFeatures`	Detect LOAM feature points from 3-D lidar data
`detectRectangularPlanePoints`	Detect rectangular plane of specified dimensions in point cloud
`detectRoadAngles`	Detect road angles in point cloud

Register Point Clouds

`pcregisterloam`	Register two point clouds using LOAM algorithm
`pcregisterfgr`	Register two point clouds using FGR algorithm
`pcregistericp`	Register two point clouds using ICP algorithm
`pcregistercpd`	Register two point clouds using CPD algorithm
`pcregistercorr`	Register two point clouds using phase correlation
`pcregisterndt`	Register two point clouds using NDT algorithm

Convert Point Cloud

`pc2dem`	Create digital elevation model (DEM) of point cloud data
`pc2scan`	Convert 3-D point cloud into 2-D lidar scan
`pc2surfacemesh`	Construct surface mesh from 3-D point cloud

Surface Mesh Workflow

`surfaceMesh`	Create surface mesh
`pc2surfacemesh`	Construct surface mesh from 3-D point cloud
`readSurfaceMesh`	Read 3-D surface mesh data from STL or PLY file
`writeSurfaceMesh`	Write 3-D surface mesh into STL or PLY file
`surfaceMeshShow`	Display surface mesh
`smoothSurfaceMesh`	Smooth surface mesh
`clusterConnectedFaces`	Cluster connected faces

Storage

`lidarParameters`	Lidar sensor parameters
`lidarPointAttributes`	Object for storing lidar point attributes

Topics

Introduction to Lidar Processing
High-level overview of lidar concepts and applications.
Coordinate Systems in Lidar Toolbox
Overview of coordinate systems in Lidar Toolbox.
What are Organized and Unorganized Point Clouds?
Define unorganized and organized point clouds and how to convert the former to latter.
Get Started with Lidar Viewer
Interactively visualize and analyze lidar data.
Create Custom Preprocessing Workflow with Lidar Viewer
Create custom preprocessing workflows for interactive use within the app.
Estimate Transformation Between Two Point Clouds Using Features
This example shows how to estimate a rigid transformation between two point clouds.

Featured Examples

Export Point Cloud Segmentations to KML Files

Export segmented point cloud data to a Keyhole Markup Language (KML) file and read and visualize the KML data using different tools.

Open Live Script

Unorganized to Organized Conversion of Point Clouds Using Spherical Projection

Convert unorganized point clouds to organized format using spherical projection.

Open Live Script

Create, Process, and Export Digital Surface Model from Lidar Data

Process aerial lidar data received from an airborne lidar system into a GeoTIFF file. Import a LAZ file containing aerial lidar data, create a spatially referenced digital surface model (DSM) from the data, crop the DSM to an area of interest, and export the cropped DSM to a GeoTIFF file.

Open Live Script

Extract On-Road and Off-Road Points from Point Cloud

Extract on-road and off-road points from point cloud data.

Open Live Script

Transform Point Cloud Using Lidar Viewer

Create a custom algorithm to transform a point cloud by using the Lidar Viewer app.

Open Live Script