Preprocessing

Downsample, median filter, transform, extract features from, and align 3-D point clouds

从激光雷达点云数据的传感器应用ons 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 theUnorganized to Organized Conversion of Point Clouds Using Spherical Projectionworkflow.

Apps

Lidar Viewer

Visualize and analyze lidar data

Functions

`pcdownsample`	Downsample a 3-D point cloud
`pcmedian`	Median filtering 3-D point cloud data
`pcdenoise`	Remove noise from 3-D point cloud
`pcalign`	Align an array point clouds
`pccat`	Concatenate 3-D point cloud array
`pcnormals`	Estimate normals for point cloud
`pctransform`	Transform 3-D point cloud
`pcorganize`	Convert 3-D point cloud into organized point cloud
`lidarParameters`	Lidar sensor parameters
`lidarPointAttributes`	Object for storing lidar point attributes
`pcregisterloam`	Register two point clouds using LOAM algorithm
`pc2dem`	Create digital elevation model (DEM) of point cloud data
`pc2scan`	Convert 3-D point cloud into 2-D lidar scan

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

`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
`removeInvalidPoints`	Remove invalid points from point cloud

`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

Topics

Introduction to Lidar
High-level overview of lidar concepts and applications.
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.
What are Organized and Unorganized Point Clouds?
Define unorganized and organized point clouds and how to convert the former to latter.

Featured Examples

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