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Lidar Point Cloud Processing

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

Point cloud data from a lidar sensor has applications 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.

You can use the extractFPFHFeatures function to extract fast point feature histogram (FPFH) descriptors from a 3-D point cloud. These feature descriptors describe the local geometry around the associated points in a point cloud.


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pcdownsampleDownsample a 3-D point cloud
pcmedianMedian filtering 3-D point cloud data
pcalignAlign an array point clouds
pccatConcatenate 3-D point cloud array
pcnormalsEstimate normals for point cloud
pctransformTransform 3-D point cloud
findNearestNeighborsFind nearest neighbors of a point in point cloud
findNeighborsInRadiusFind neighbors within a radius of a point in the point cloud
findPointsInROIFind points within a region of interest in the point cloud
removeInvalidPointsRemove invalid points from point cloud
extractFPFHFeaturesExtract fast point feature histogram (FPFH) descriptors from point cloud
detectRectangularPlanePointsDetect rectangular plane of specified dimensions in point cloud

Featured Examples