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 and autonomous driving 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
To create and process surface mesh data, use the
surfaceMeshobject. Lidar Toolbox includes functions that read, write, and visualize a surface mesh.
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
pcorganizefunction. 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 interactively visualize, analyze, and preprocess point cloud data using the Lidar Viewer app.
|Lidar Viewer||Visualize and analyze lidar data|
|Downsample a 3-D point cloud|
|Median filtering 3-D point cloud data|
|Remove noise from 3-D point cloud|
|Remove invalid points from point cloud|
|Align array of point clouds|
|Concatenate 3-D point cloud array|
|Estimate normals for point cloud|
|Transform 3-D point cloud|
Surface Mesh Workflow
Organize Point Cloud
Find Points in Point Cloud
Detect and Extract Features
|Extract eigenvalue-based features from point cloud segments|
|Extract fast point feature histogram (FPFH) descriptors from point cloud|
|Detect ISS feature points in point cloud|
|Detect LOAM feature points from 3-D lidar data|
|Detect rectangular plane of specified dimensions in point cloud|
|Detect road angles in point cloud|
Register Point Cloud
Convert Point Cloud
- 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.
- Extract On-Road and Off-Road Points from Point Cloud
This example shows how to extract on-road and off-road points from point cloud data.