Getting Started / Basic Structures

The basic data type in PCL 1.x is a PointCloud. A PointCloud is a C++ class which contains the following data fields:

• width (int)

  Specifies the width of the point cloud dataset in the number of points. width has two meanings:

  • it can specify the total number of points in the cloud (equal with the number of elements in points – see below) for unorganized datasets;

  • it can specify the width (total number of points in a row) of an organized point cloud dataset.

  Note

  An organized point cloud dataset is the name given to point clouds that resemble an organized image (or matrix) like structure, where the data is split into rows and columns. Examples of such point clouds include data coming from stereo cameras or Time Of Flight cameras. The advantages of an organized dataset is that by knowing the relationship between adjacent points (e.g. pixels), nearest neighbor operations are much more efficient, thus speeding up the computation and lowering the costs of certain algorithms in PCL.

  Note

  An projectable point cloud dataset is the name given to point clouds that have a correlation according to a pinhole camera model between the (u,v) index of a point in the organized point cloud and the actual 3D values. This correlation can be expressed in it’s easiest form as: u = f*x/z and v = f*y/z

  Examples:

  cloud.width = 640; // there are 640 points per line
  

• height (int)

  Specifies the height of the point cloud dataset in the number of points. height has two meanings:

  • it can specify the height (total number of rows) of an organized point cloud dataset;

  • it is set to 1 for unorganized datasets (thus used to check whether a dataset is organized or not).

  Example:

  cloud.width = 640; // Image-like organized structure, with 480 rows and 640 columns,
  cloud.height = 480; // thus 640*480=307200 points total in the dataset
  

  Example:

  cloud.width = 307200;
  cloud.height = 1; // unorganized point cloud dataset with 307200 points
  

• points (std::vector<PointT>)

  Contains the data array where all the points of type PointT are stored. For example, for a cloud containing XYZ data, points contains a vector of pcl::PointXYZ elements:

  pcl::PointCloud<pcl::PointXYZ> cloud;
  std::vector<pcl::PointXYZ> data = cloud.points;
  

• is_dense (bool)

  Specifies if all the data in points is finite (true), or whether the XYZ values of certain points might contain Inf/NaN values (false).

• sensor_origin_ (Eigen::Vector4f)

  Specifies the sensor acquisition pose (origin/translation). This member is usually optional, and not used by the majority of the algorithms in PCL.

• sensor_orientation_ (Eigen::Quaternionf)

  Specifies the sensor acquisition pose (orientation). This member is usually optional, and not used by the majority of the algorithms in PCL.

To simplify development, the PointCloud class contains a number of helper member functions. For example, users don’t have to check if height equals 1 or not in their code in order to see if a dataset is organized or not, but instead use PointCloud:

if (!cloud.isOrganized ())
  ...

The PointT type is the primary point data type and describes what each individual element of points holds. PCL comes with a large variety of different point types, most explained in the Adding your own custom PointT type tutorial.

Compiling your first code example

Until we find the right minimal code example, please take a look at the Using PCL in your own project and Writing a new PCL class tutorials to see how to compile and write code for or using PCL.