Point clouds appeared in the robotics toolbox as a way to intuitively represent and manipulate the information provided by 3D sensors, such as time-of-flight cameras and laser scanners, in which the space is sampled in a finite set of points in a 3D frame of reference. The Point Cloud Library (PCL) provides a number of data types and data structures to easily represent not only the points of our sampled space, but also the different properties of the sampled space, such as color, normal vectors, and so on. PCL also provides a number of state-of-the-art algorithms to perform data processing on our data samples, such as filtering, model estimation, surface reconstruction, and much more.

ROS provides a message-based interface through which PCL point clouds can be efficiently communicated, and a set of conversion functions from native PCL types to ROS messages, in much the same way as it is done with OpenCV images. Aside from the standard capabilities of the ROS API, there...