Cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

Getting started

Cartographer is a standalone C++ library. To get started quickly, use our ROS integration.

Getting started with ROS

ROS integration is provided by the Cartographer ROS repository. You will find complete documentation for using Cartographer with ROS at the Cartographer ROS Read the Docs site.

Getting started without ROS

Please see our ROS integration as a starting point for integrating your system with the standalone library. Currently, it is the best available reference.

On Ubuntu 14.04 (Trusty):

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# Install the required libraries that are available as debs.
sudo apt-get update
sudo apt-get install -y \
    cmake \
    g++ \
    git \
    google-mock \
    libboost-all-dev \
    libcairo2-dev \
    libeigen3-dev \
    libgflags-dev \
    libgoogle-glog-dev \
    liblua5.2-dev \
    libprotobuf-dev \
    libsuitesparse-dev \
    libwebp-dev \
    ninja-build \
    protobuf-compiler \
    python-sphinx
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# Build and install Ceres.
git clone https://ceres-solver.googlesource.com/ceres-solver
cd ceres-solver
mkdir build
cd build
cmake .. -G Ninja
ninja
ninja test
sudo ninja install
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# Build and install Cartographer.
cd cartographer
mkdir build
cd build
cmake .. -G Ninja
ninja
ninja test
sudo ninja install

System Requirements

Although Cartographer may run on other systems, it is confirmed to be working on systems that meet the following requirements:

  • 64-bit, modern CPU (e.g. 3rd generation i7)
  • 16 GB RAM
  • Ubuntu 14.04 (Trusty) and 16.04 (Xenial)
  • gcc version 4.8.4 and 5.4.0

Known Issues

  • 32-bit builds have libeigen alignment problems which cause crashes and/or memory corruptions.

How to cite us

Background about the algorithms developed for Cartographer can be found in the following publication. If you use Cartographer for your research, we would appreciate it if you cite our paper.

W. Hess, D. Kohler, H. Rapp, and D. Andor, Real-Time Loop Closure in 2D LIDAR SLAM, in Robotics and Automation (ICRA), 2016 IEEE International Conference on. IEEE, 2016. pp. 1271–1278.