LiDAR (Light and Ranging Detection) technology has now become the quintessential technique for collecting geospatial data from the earth's surface. This code implements a method for point cloud partitioning based on distributed memory and MPI (Message Passing Interface) technology.

Original project: https://gitlab.citius.usc.es/lidar/rule-based-classifier.

git clone https://github.com/GarciaBarreiro/octree-mpi.git
cd octree-mpi

• Eigen, Armadillo and OpenMPI
  • Ubuntu

    sudo apt install libeigen3-dev libarmadillo-dev openmpi-bin openmpi-common openssh-client openssh-server libopenmpi1.3 libopenmpi-dbg libopenmpi-dev

  • ArchLinux

    sudo pacman -S eigen openmpi
    git clone https://aur.archlinux.org/armadillo.git armadillo
    (cd armadillo && makepkg -si --noconfirm)

The following commands must be executed in the root folder of the project.

• LASTools:

  wget https://lastools.github.io/download/LAStools_221128.zip && unzip LAStools_221128.zip -d ./lib && rm LAStools_221128.zip

• LASlib:

  (cd lib/LAStools && cmake . && make)

The recommended way to compile the project is through CMake. In the project directory, just execute

(cmake -S . -B build -DCMAKE_BUILD_TYPE=Release && cd build && make)

This creates the executable build/rule-based-classifier-cpp.

/!\ WARNING: If cmake is executed in the project directory, the already existing Makefile will be overwritten.

${path_to_binary_executable} -i data/ptR_18C.las -r search_radius [-o output_dir]

Grupo de Arquitectura de Computadores (GAC)
Centro Singular de Investigación en Tecnologías Inteligentes (CiTIUS)
Universidad de Santiago de Compostela (USC)

Maintainers:

• Miguel Yermo García ([email protected])
• Silvia Rodríguez Alcaraz ([email protected])