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| Point Cloud Library | |
|---|---|
_logo_with_text.png){kind=logo} | |
| Original author | Willow Garage |
| Initial release | March 2010; 15 years ago (2010-03)[1][2] |
| Stable release | |
| Operating system | Cross-platform |
| Type | Library |
| License | BSD license |
| Website | pointclouds |
| Repository | |
ThePoint Cloud Library (PCL) is anopen-sourcelibrary of algorithms forpoint cloud processing tasks and 3Dgeometry processing, such as occur in three-dimensionalcomputer vision. The library contains algorithms for filtering, feature estimation, surface reconstruction,3D registration,[5]model fitting,object recognition, andsegmentation. Each module is implemented as a smaller library that can be compiled separately (for example, libpcl_filters, libpcl_features, libpcl_surface, ...). PCL has its own data format for storing point clouds -PCD (Point Cloud Data), but also allows datasets to be loaded and saved in many other formats. It is written inC++ and released under theBSD license.
These algorithms have been used, for example, forperception inrobotics to filter outliers from noisy data,stitch 3D point clouds together, segment relevant parts of a scene, extract keypoints and compute descriptors to recognize objects in the world based on their geometric appearance, and create surfaces from point clouds and visualize them.[6][failed verification]
PCL requires severalthird-party libraries to function, which must be installed. Most mathematical operations are implemented using theEigen library. The visualization module for 3D point clouds is based onVTK.Boost is used for shared pointers and theFLANN library for quick k-nearest neighbor search. Additional libraries such as Qhull,OpenNI, orQt are optional and extend PCL with additional features.
PCL iscross-platform software that runs on the most commonly usedoperating systems:Linux,Windows,macOS andAndroid. The library is fully integrated with theRobot Operating System (ROS) and provides support forOpenMP andIntelThreading Building Blocks (TBB) libraries for multi-coreparallelism.[7][8]
The library is constantly updated and expanded, and its use in various industries is constantly growing. For example, PCL participated in theGoogle Summer of Code 2020 initiative with three projects. One was the extension of PCL for use withPython using Pybind11.[9]
A large number of examples and tutorials are available on the PCL website, either as C++ source files or as tutorials with a detailed description and explanation of the individual steps.
Point cloud library is widely used in many different fields, here are some examples:
PCL requires for its installation several third-party libraries, which are listed below. Some libraries are optional and extend PCL with additional features. The PCL library is built with theCMake build system (http://www.cmake.org/) at least in version 3.5.0.[10][8]
Mandatory libraries:
Optional libraries that enable some additional features:
ThePCD (Point Cloud Data) is a file format for storing 3D point cloud data. It was created because existing formats did not support some of the features provided by the PCL library. PCD is the primary data format in PCL, but the library also offers the ability to save and load data in other formats (such as PLY, IFS, VTK, STL, OBJ, X3D). However, these other formats do not have the flexibility and speed of PCD files. One of the PCD advantages is the ability to store and process organized point cloud datasets. Another is very fast saving and loading of points that are stored in binary form.[11][12]
The PCD version is specified with the numbers 0.x (e.g., 0.5, 0.6, etc.) in the header of each file. The official version in 2020 is PCD0.7 (PCD_V7). The main difference compared to version 0.6 is that a new header - VIEWPOINT has been added. It specifies the information about the orientation of the sensor relative to the dataset.[13]
The PCD file is divided into two parts -header anddata. The header has a precisely defined format and contains the necessary information about the point cloud data that are stored in it. The header must be encoded in ASCII, however, the data can be stored in ASCII or binary format. Thanks to the fact that the ASCII format is more human readable, it can be opened in standard software tools and easily edited.
In version 0.7 theversion of the PCD file is at the beginning of the header, followed by thename,size, andtype of each dimension of the stored data. It also shows a number of points (height*width) in the whole cloud and information about whether the point cloud dataset is organized or unorganized. Thedata type specifies in which format the point cloud data are stored (ASCII or binary). The header is followed by a set of points. Each point can be stored on a separate line (unorganized point-cloud) or they are stored in an image-like organized structure (organized point-cloud).[11] More detailed information about header entries can be found indocumentation. Below is an example of a PCD file. The order of header entries is important!
# .PCD v.7 - Point Cloud Data file formatVERSION .7FIELDS x y z rgbSIZE 4 4 4 4TYPE F F F FCOUNT 1 1 1 1WIDTH 213HEIGHT 1VIEWPOINT 0 0 0 1 0 0 0POINTS 213DATA ascii0.93773 0.33763 0 4.2108e+060.90805 0.35641 0 4.2108e+060.81915 0.32 0 4.2108e+060.97192 0.278 0 4.2108e+06......
The development of the Point Cloud Library started in March 2010 atWillow Garage. The project initially resided on a sub domain of Willow Garage then moved to a new website www.pointclouds.org in March 2011.[1] PCL's first official release (Version 1.0) was released two months later in May 2011.[2]
PCL is divided into several smaller code libraries that can be compiled separately. Some of the most important modules and their functions are described below.[14][15]
When scanning a 3D point cloud, errors and various deviations can occur, which causes noise in the data. This complicates the estimation of some local point cloud characteristics, such as surface normals. These inaccuracies can lead to significant errors in further processing and it is therefore advisable to remove them with a suitable filter. Thepcl_filters library provides several useful filters for removing outliers and noise and also downsampling the data. Some of them use simple criteria to trim points, others use statistical analysis.
Thepcl_features library contains algorithms and data structures for 3Dfeature estimation. Mostly used local geometric features are the point normal and underlying surface's estimated curvature. The features describe geometrical patterns at a certain point based on selected k-neighborhood (data space selected around the point). The neighborhood can be selected by determining a fixed number of points in the closest area or defining a radius of a sphere around the point.
One of the easiest implemented methods for estimating the surface normal is an analysis of theeigenvectors andeigenvalues of acovariance matrix created from the neighborhood of the point. Point Feature Histograms (or faster FPFH) descriptors are an advanced feature representation and depend on normal estimations at each point. It generalizes the mean curvature around the point using a multidimensional histogram of values. Some of other descriptors in the library are Viewpoint Feature Histogram (VFH) descriptor, NARF descriptors, Moment of inertia and eccentricity based descriptors, Globally Aligned Spatial Distribution (GASD) descriptors, and more.
Thepcl_segmentation library contains algorithms for segmenting a point cloud into different clusters. Clustering is often used to divide the cloud into individual parts, that can be further processed. There are implemented several classes, that support various segmentation methods:
Thepcl_visualization library is used to quickly and easily visualize 3D point cloud data. The package makes use of the VTK library for 3D rendering of clouds and range images. The library offers:
Registration is the problem of aligning various point cloud datasets acquired from different views into a single point cloud model. Thepcl_registration library implements number of point cloud registration algorithms for both organized and unorganized datasets. The task is to identify the corresponding points between the data sets and find a transformation that minimizes their distance.
Theiterative closest point algorithm minimizes the distances between the points of two pointclouds. It can be used for determining if one PointCloud is just a rigid transformation of another.Normal Distributions Transform (NDT) is a registration algorithm that can be used to determine a rigid transformation between two point clouds that have over 100,000 points.
Thesample_consensus library holds SAmple Consensus (SAC) methods like RANSAC and models to detect specific objects in point clouds. Some of the models implemented in this library include plane models that are often used to detect interior surfaces such as walls and floors. Next models are the lines, 2D and 3D circles in a plane, sphere, cylinder, cone, a model for determining a line parallel with a given axis, a model for determining a plane perpendicular to a user-specified axis, plane parallel to a user-specified axis, etc. These can be used to detect objects with common geometric structures (e.g., fitting a cylinder model to a mug).
Robust sample consensus estimators that are available in the library:
Several algorithms for surface reconstruction of 3D point clouds are implemented in thepcl_surface library. There are several ways to reconstruct the surface. One of the most commonly used is meshing, and the PCL library has two algorithms: very fast triangulation of original points and slower networking, which also smooths and fills holes. If the cloud is noisy, it is advisable to use surface smoothing using one of the implemented algorithms.
TheMoving Least Squares (MLS) surface reconstruction method is a resampling algorithm that can reconstruct missing parts of a surface. Thanks to higher order polynomial interpolations between surrounding data points, MLS can correct and smooth out small errors caused by scanning.
Greedy Projection Triangulation implements an algorithm for fast surface triangulation on an unordered PointCloud with normals. The result is a triangle mesh that is created by projecting the local neighborhood of a point along the normal of the point. It works best if the surface is locally smooth and there are smooth transitions between areas with different point densities. Many parameters can be set that are taken into account when connecting points (how many neighbors are searched, the maximum distance for a point, minimum and maximum angle of a triangle).
The library also implements functions for creating aconcave orconvex hull polygon for a plane model, Grid projection surface reconstruction algorithm,marching cubes,ear clipping triangulation algorithm,Poisson surface reconstruction algorithm, etc.
Theio_library allows you to load and save point clouds to files, as well as capture clouds from various devices. It includes functions that allow you to concatenate the points of two different point clouds with the same type and number of fields. The library can also concatenate fields (e.g., dimensions) of two different point clouds with same number of points.
Starting withPCL 1.0 the library offers a new generic grabber interface that provides easy access to different devices and file formats. The first devices supported for data collection wereOpenNI compatible cameras (tested withPrimesense Reference Design,Microsoft Kinect andAsus Xtion Pro cameras). As ofPCL 1.7, point cloud data can be also obtained from theVelodyne High Definition LiDAR (HDL) system, which produces 360 degree point clouds. PCL supports both the originalHDL-64e andHDL-32e. There is also a new driver forDinast Cameras (tested withIPA-1110,Cyclopes II andIPA-1002 ng T-Less NG).PCL 1.8 brings support for IDS-ImagingEnsenso cameras,DepthSense cameras (e.g.Creative Senz3D,DepthSense DS325), anddavidSDK scanners.
Thepcl_kdtree library provides thekd-tree data-structure for organizing a set of points in a space with k dimensions. Used to find the K nearest neighbors (using FLANN) of a specific point or location.
Thepcl_octree library implements theoctree hierarchical tree data structure for point cloud data. The library provides nearest neighbor search algorithms, such as “Neighbors within Voxel Search”, “K Nearest Neighbor Search” and “Neighbors within Radius Search”. There are also several octree types that differ by their leaf node's properties. Each leaf node can hold a single point or a list of point indices, or it does not store any point information. The library can be also used for detection of spatial changes between multiple unorganized point clouds by recursive comparison of octet tree structures.
Thepcl_search library implements methods for searching for nearest neighbors using different data structures, that can be found in other modules, such as KdTree, Octree, or specialized search for organized datasets.
Therange_image library contains two classes for representing and working with range images whose pixel values represent a distance from the sensor. The range image can be converted to a point cloud if the sensor position is specified or the borders can be extracted from it.
Thepcl_keypoints library contains implementations of point cloud keypoint detection algorithms (AGAST corner point detector,Harris detector, BRISK detector, etc.).
Thepcl_common library contains the core data structures for point cloud, types for point representation, surface normals, RGB color values, etc. There are also implemented useful methods for computing distances, mean values and covariance, geometric transformations, and more. The common library is mainly used by other PCL modules.
