Lidar and Point Cloud Processing

Downsample, denoise, transform, visualize, register, and fit geometrical shapes of 3-D point clouds

Point clouds are typically used to measure physical world surfaces. They have applications in robot navigation and perception, depth estimation, stereo vision, visual registration, and in advanced driver assistance systems (ADAS). Computer Vision Toolbox™ algorithms provide point cloud processing functionality for downsampling, denoising, and transforming point clouds. The toolbox also provides point cloud registration, geometrical shape fitting to 3-D point clouds, and the ability to read, write, store, display, and compare point clouds. You can also combine multiple point clouds to reconstruct a 3-D scene using the iterative closest point (ICP) algorithm.

You can use pcregistercpd, pcregistericp, and pcregisterndt to register a moving point cloud to a fixed point cloud. These registration algorithms are based on the Coherent Point Drift (CPD) algorithm, the Iterative Closest Point (ICP) algorithm and the Normal-Distributions Transform (NDT) algorithm, respectively. Best performance requires adjusting properties for your data. Before using the point cloud registration functions, consider using pcdownsample to downsample your point clouds, which improves the accuracy and efficiency of registration.

Functions

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Preprocess Point Cloud

`pcbin`	Spatially bin point cloud points
`pcdenoise`	Remove noise from 3-D point cloud
`pcdownsample`	Downsample a 3-D point cloud
`pcnormals`	Estimate normals for point cloud
`pcmerge`	Merge two 3-D point clouds
`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
`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

Register Point Clouds

`createPoseGraph`	Create pose graph
`optimizePoses`	Optimize absolute poses using relative pose constraints
`pcdownsample`	Downsample a 3-D point cloud
`pctransform`	Transform 3-D point cloud
`pcregistericp`	Register two point clouds using ICP algorithm
`pcregistercpd`	Register two point clouds using CPD algorithm
`pcregisterndt`	Register two point clouds using NDT algorithm
`rigid3d`	3-D rigid geometric transformation

Fit Point Clouds to Geometric Models

`pcfitcylinder`	Fit cylinder to 3-D point cloud
`pcfitplane`	Fit plane to 3-D point cloud
`pcfitsphere`	Fit sphere to 3-D point cloud
`pcnormals`	Estimate normals for point cloud
`fitPolynomialRANSAC`	Fit polynomial to points using RANSAC
`ransac`	Fit model to noisy data
`cylinderModel`	Object for storing a parametric cylinder model
`planeModel`	Object for storing a parametric plane model
`sphereModel`	Object for storing a parametric sphere model

Read and Write Point Clouds

`pcread`	Read 3-D point cloud from PLY or PCD file
`pcwrite`	Write 3-D point cloud to PLY or PCD file
`pcfromkinect`	Point cloud from Kinect for Windows
`velodyneFileReader`	Read point cloud data from Velodyne PCAP file

Visualize Point Clouds

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

Store Point Cloud

`pcviewset`	Manage data for point cloud based visual odometry and SLAM
`pointCloud`	Object for storing 3-D 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

Topics

The PLY Format

The Stanford Triangle Format

Point Cloud Registration Overview

Understand point cloud registration workflow.

Getting Started with Point Clouds Using Deep Learning

Understand how to use point clouds for deep learning.

Featured Examples

Build a Map from Lidar Data Using SLAM

Process lidar data to build a map and estimate a vehicle trajectory using simultaneous localization and mapping.

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Build a Map from Lidar Data

Process 3-D lidar sensor data to progressively build a map, with assistance from inertial measurement unit (IMU) readings.

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Structure From Motion From Two Views

Estimate 3-D structure of a scene from a set of 2-D imges.

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Structure From Motion From Multiple Views

Structure from motion (SfM) is the process of estimating the 3-D structure of a scene from a set of 2-D views. It is used in many applications, such as robot navigation, autonomous driving, and augmented reality. This example shows you how to estimate the poses of a calibrated camera from a sequence of views, and reconstruct the 3-D structure of the scene up to an unknown scale factor.

Open Live Script