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preprocessing -凯发k8网页登录

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 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 function. for more information on the distinctions between organized and unorganized point clouds, see

  • 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 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 viewervisualize and analyze lidar data

functions

downsample a 3-d point cloud
median filtering 3-d point cloud data
remove noise from 3-d point cloud
find points within a cylindrical region in a point cloud
remove invalid points from point cloud
remove hidden 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
undistort point cloud affected by ego motion
point cloud made from discrete blocks
datastore for use with blocks from blockedpointcloud objects
convert 3-d point cloud into organized point cloud
find nearest neighbors of a point in point cloud
find neighbors within a radius of a point in the point cloud
find points within a region of interest in the point cloud
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 two point clouds using loam algorithm
register two point clouds using fgr algorithm
register two point clouds using icp algorithm
register two point clouds using cpd algorithm
register two point clouds using phase correlation
register two point clouds using ndt algorithm
create digital elevation model (dem) of point cloud data
convert 3-d point cloud into 2-d lidar scan
construct surface mesh from 3-d point cloud
create surface mesh
construct surface mesh from 3-d point cloud
read 3-d surface mesh data from stl or ply file
write 3-d surface mesh into stl or ply file
display surface mesh
smooth surface mesh
cluster connected faces
lidar sensor parameters
object for storing lidar point attributes

topics


  • high-level overview of lidar concepts and applications.


  • overview of coordinate systems in lidar toolbox.


  • 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 workflows for interactive use within the app.


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

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