Getting stareted

PCL Julia packages follows the PCL module structure. If you need a particular module (e.g. PCLCommon.jl), then you can use the specific module(s) as follows:

using PCLCommon

If you want to import all the PCL packages, then:

using PCL

Note that it will take a few times to load (30 seconds~) since it compiles all the PCL packages.

PointCloud{PointT}

The most frequently used type would be PointCloud(defined in PCLCommon.jl), which represents arbitary point type of point cloud (pcl::PointCloud<PointT>::Ptr in C++). In this section we will show the basic usage of PointCloud type quickly.

Create an empty point cloud

using PCLCommon

cloud_xyz = PointCloud{PointXYZ}()

For different point types, just change the type parameter as follows:

cloud_rgba = PointCloud{PointXYZRGBA}()

Create a point cloud with specified size

cloud_xyz = PointCloud{PointXYZ}(100, 200) # width=100, height=200

IO

Load a PCD file

using PCLCommon
using PCLIO

cloud_xyz = PointCloud{PointXYZ}("your_pcd_data.pcd")

needs PCLIO.jl in addition to PCLCommon.jl.

Filtering

PassThrough filter

using PCLCommon
using PCLIO
using PCLFilters

cloud = PointCloud{PointXYZRGB}("your_pcd_file.pcd")
cloud_filtered = PointCloud{PointXYZRGB}()

pass = PassThrough{PointXYZRGB}()
setInputCloud(pass, cloud)
setFilterFieldName(pass, "z")
setFilterLimits(pass, 0.0, 1.0)
filter(pass, cloud_filtered)

needs PCLFilters.jl.

Visualization

using PCLCommon
using PCLIO
using PCLVisualization

cloud = PointCloud{PointXYZRGB}("your_pcd_file.pcd")
viewer = PCLVisualizer("pcl visualizer")
addPointCloud(viewer, cloud, id="input")
spin(viewer) # you will see the PCLVisualizer

needs PCLVisualization.jl.

Examples and tutorials

See JuliaPCL/PCL/test directory for more examples. It includes more complex filtering, feature extraction, recognition, tracking and visualization examples and also some PCL tutorial translations as well.