nanoflann

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ROS 2 Lyrical (u26.04)
ROS 2 Rolling (u26.04)

(Binary build badges are for amd64 and arm64, respectively)

1. About

nanoflann is a header-only C++ library for building KD-Trees of datasets with different topologies. The Euclidean core (R², R³, R^N point clouds) is C++11; the optional compile-time product-manifold metrics — SO(2), SO(3), SE(2), SE(3), the unit sphere S²/S^N, tori, and arbitrary products — require C++17 (see §1.5). It also provides incremental / dynamic KD-tree indices for point clouds that change over time (e.g. sliding-window LiDAR maps). nanoflann returns exact nearest neighbors by default (an optional eps-approximate mode is available via SearchParameters::eps). It 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.

Citing nanoflann

If you use nanoflann in your research, please cite the following paper (published in IEEE RA-L, 2026):

J.L. Blanco-Claraco, “nanoflann: A Header-Only KD-Tree Library for Exact Nearest-Neighbor Search on Compile-Time Product Manifolds,” IEEE Robotics and Automation Letters, 2026, doi: 10.1109/LRA.2026.3747624. IEEE Xplore. Preprint (fallback): arXiv:2610.15215.

@article{blancoclaraco2026nanoflann,
  title   = {nanoflann: A Header-Only {KD}-Tree Library for Exact Nearest-Neighbor Search on Compile-Time Product Manifolds},
  author  = {Blanco-Claraco, Jose Luis},
  journal = {IEEE Robotics and Automation Letters},
  year    = {2026},
  doi     = {10.1109/LRA.2026.3747624},
  url     = {https://ieeexplore.ieee.org/document/11753906},
  note    = {Preprint: arXiv:2610.15215}
}

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
• conda-forge users (Linux, macOS, Windows): conda install -c conda-forge 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

File truncated at 100 lines see the full file