libnabo is a fast K Nearest Neighbour library for low-dimensional spaces. It provides a clean, legacy-free, scalar-type–agnostic API thanks to C++ templates. Its current CPU implementation is strongly inspired by ANN, but with more compact data types. On the average, libnabo is 5% to 20% faster than ANN.

libnabo depends on Eigen, a modern C++ matrix and linear-algebra library. libnabo works with either version 2 or 3 of Eigen. libnabo also depends on Boost, a C++ general library.

libnabo was developed by Stéphane Magnenat as part of his work at ASL-ETH and is now maintained by Simon Lynen.

Download

Ubuntu builds are available on my PPA at: https://launchpad.net/~stephane.magnenat They provide a package with the shared library, another with the development headers and a third with the documentation.

The source code is available from github, you can clone the git tree by doing:

git clone git://github.com/ethz-asl/libnabo.git

You can then checkout a specific branch, for instance to checkout version 1.0.2, do:

cd libnabo
git checkout 1.0.2

Compilation

libnabo uses CMake as build system. The complete compilation process depends on the system you are using (Linux, Mac OS X or Windows). You will find a nice introductory tutorial in this video.

Prerequisites

If your operating system does not provide it, you must get Eigen and Boost. Eigen only needs to be downloaded and extracted. You also need grep, which is available in standard on Linux or Mac OS X, you can get the window version here.

Compilation options

libnabo provides the following compilation options, available through CMake:

• SHARED_LIBS (boolean, default: false): if true, build a shared library, otherwise build a static library

You can specify them with a command-line tool, ccmake, or with a graphical tool, cmake-gui. Please read the CMake documentation for more information.

Quick compilation and installation under Unix

Under Unix, assuming that Eigen and Boost are installed system-wide, you can compile (with optimisation and debug information) and install libnabo in /usr/local with the following commands run in the top-level directory of libnabo's sources:

SRC_DIR=`pwd`
BUILD_DIR=${SRC_DIR}/build
mkdir -p ${BUILD_DIR} && cd ${BUILD_DIR}
cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ${SRC_DIR}
# if Eigen or Boost are not available system-wide, run at that point: 
#   cmake-gui .
# cmake-gui allows you to tell the location of Eigen or Boost
make
sudo make install

These lines will compile libnabo in a build sub-directory and therefore keep your source tree clean. Note that you could compile libnabo anywhere you have write access, such as in /tmp/libnabo. This out-of-source build is a nice feature of CMake. If Eigen or Boost are not installed system-wide, you might have to tell CMake where to find them (using ccmake or cmake-gui).

You can generate the documentation by typing:

make doc

Usage

libnabo is easy to use. For example, assuming that you are working with floats and that you have a point set M and a query point q, you can find the K nearest neighbours of q in M:

#include "nabo/nabo.h"
using namespace Nabo;
using namespace Eigen;
...
NNSearchF* nns = NNSearchF::createKDTreeLinearHeap(M);

const int K = 5;
VectorXi indices(K);
VectorXf dists2(K);

nns->knn(q, indices, dists2, K);

In this example, M is an Eigen (refering to the software, not to the math) matrix (column major, float) and q is an Eigen vector (float). The results indices and dists2 are Eigen vectors of indices and squared distances refering to the columns of M. See examples/trivial.cpp for a compilable version of this example, and examples/usage.cpp for a slightly more complex example involving multi-point queries.

Running make doc in your build directory will generate a browsable documentation in doc/html. The main page doc/html/index.html contains a detailed overview of the usage of libnabo.

Python bindings

libnabo includes python bindings that are compiled if python is available. The resulting module is called pynabo, you can see an example in python/test.py. You can find more information in the docstring-based documentation:

python -c "import pynabo; help(pynabo.NearestNeighbourSearch)"

Unit testing

The distribution of libnabo integrates a unit test module, based on CTest. Just type:

make test

...in the build directory to run the tests. Their outputs are available in the Testing directory. These consist of validation and benchmarking tests. If ANN or FLANN are detected when compiling libnabo, make test will also perform comparative benchmarks.

Citing libnabo

If you use libnabo in the academic context, please cite this paper that evaluates its performances in the contex of ICP:

@article{elsebergcomparison,
	title={Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration},
	author={Elseberg, J. and Magnenat, S. and Siegwart, R. and N{\"u}chter, A.},
	journal={Journal of Software Engineering for Robotics (JOSER)},
	pages={2--12},
	volume={3},
	number={1},
	year={2012},
	issn={2035-3928}
}

Bug reporting

Please use github's issue tracker to report bugs.

License

libnabo is released under a permissive BSD license.