nanoflann

Distro	Build dev	Build releases	Stable version
ROS 2 Humble (u22.04)		amd64 arm64
ROS 2 Jazzy (u24.04)		amd64 arm64
ROS 2 Kilted (u24.04)		amd64 arm64
ROS 2 Lyrical (u26.04)		amd64 arm64
ROS 2 Rolling (u26.04)		amd64 arm64

1. About

nanoflann is a C++11 header-only library for building KD-Trees of datasets with different topologies: R², R³ (point clouds), SO(2) and SO(3) (2D and 3D rotation groups). No support for approximate NN is provided. nanoflann does not require compiling or installing. You just need to #include <nanoflann.hpp> in your code.

This library is a fork of the flann library by Marius Muja and David G. Lowe, and born as a child project of MRPT. Following the original license terms, nanoflann is distributed under the BSD license. Please, for bugs use the issues button or fork and open a pull request.

Cite as:

@misc{blanco2014nanoflann,
  title        = {nanoflann: a {C}++ header-only fork of {FLANN}, a library for Nearest Neighbor ({NN}) with KD-trees},
  author       = {Blanco, Jose Luis and Rai, Pranjal Kumar},
  howpublished = {\url{https://github.com/jlblancoc/nanoflann}},
  year         = {2014}
}

See the release CHANGELOG for a list of project changes.

1.1. Obtaining the code

• Easiest way: clone this GIT repository and take the include/nanoflann.hpp file for use where you need it.
• Debian or Ubuntu (21.04 or newer) users can install it simply with:

  $ sudo apt install libnanoflann-dev
  

• macOS users can install nanoflann with Homebrew with:

  $ brew install brewsci/science/nanoflann
  

or

  $ brew tap brewsci/science
  $ brew install nanoflann
  

MacPorts users can use:

  $ sudo port install nanoflann
  

• Linux users can also install it with Linuxbrew with: brew install homebrew/science/nanoflann
• List of stable releases. Check out the CHANGELOG

Although nanoflann itself doesn’t have to be compiled, you can build some examples and tests with:

$ sudo apt-get install build-essential cmake libgtest-dev libeigen3-dev
$ mkdir build && cd build && cmake ..
$ make && make test

1.2. C++ API reference

• Browse the Doxygen documentation.

• Important note: If L2 norms are used, notice that search radius and all passed and returned distances are actually squared distances.

1.3. Code examples

• KD-tree look-up with knnSearch() and radiusSearch(): pointcloud_kdd_radius.cpp
• KD-tree look-up on a point cloud dataset: pointcloud_example.cpp
• KD-tree look-up on a dynamic point cloud dataset: dynamic_pointcloud_example.cpp
• KD-tree look-up on a rotation group (SO2): SO2_example.cpp
• KD-tree look-up on a rotation group (SO3): SO3_example.cpp
• KD-tree look-up on a point cloud dataset with an external adaptor class: pointcloud_adaptor_example.cpp
• KD-tree look-up directly on an Eigen::Matrix<>: matrix_example.cpp
• KD-tree look-up directly on std::vector<std::vector<T> > or std::vector<Eigen::VectorXd>: vector_of_vectors_example.cpp
• Example with a Makefile for usage through pkg-config (for example, after doing a “make install” or after installing from Ubuntu repositories): example_with_pkgconfig/
• Example of how to build an index and save it to disk for later usage: saveload_example.cpp
• GUI examples (requires mrpt-gui, e.g. sudo apt install libmrpt-gui-dev):
  • nanoflann_gui_example_R3

1.4. Why a fork?

• Execution time efficiency:
  • The power of the original flann library comes from the possibility of choosing between different ANN algorithms. The cost of this flexibility is the declaration of pure virtual methods which (in some circumstances) impose run-time penalties. In nanoflann all those virtual methods have been replaced by a combination of the Curiously Recurring Template Pattern (CRTP) and inlined methods, which are much faster.
  • For radiusSearch(), there is no need to make a call to determine the number of points within the radius and then call it again to get the data. By using STL containers for the output data, containers are automatically resized.
  • Users can (optionally) set the problem dimensionality at compile-time via a template argument, thus allowing the compiler to fully unroll loops.
  • nanoflann allows users to provide a precomputed bounding box of the data, if available, to avoid recomputation.
  • Indices of data points have been converted from int to size_t, which removes a limit when handling very large data sets.

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