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.
|Downsample a 3-D point cloud|
|Median filtering 3-D point cloud data|
|Align an array point clouds|
|Concatenate 3-D point cloud array|
|Estimate normals for point cloud|
|Transform 3-D 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|
|Remove invalid points from point cloud|