AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

This repository contains AgriColMap, an open, research-oriented 3D map registration system for multi-robot in farming scenarios. This software has been tested using the UAV-UGV Collaborative Mapping Dataset distributed within the Flourish Sapienza Datasets collection. Please also check out our video:

Installation with OpenCV > 3.2.0 (with extra modules) and PCL >= 1.7.0

sudo apt-get install libyaml-cpp-dev
## Creating the workspace 
git clone
cd /agricolmap
git submodule update --init --recursive
mkdir build && cd build
cmake ..
make -j8


In this tutorial, we briefly show how to use the AgriColMap to register 3D maps gathered by aerial and ground robots. The files you need to download are:

Uncompress the downloaded file into: ${PATH_TO_AGRICOLMAP}/maps/. The "Soybean Dataset" contains UAV and UGV datasets registered in a soybean farm. Other datasets are freely available on Sapienza Collaborative Mapping Datasets.

cd bin
./registration_node ../params/aligner_soybean_params_row3.yaml  10 100 50 2

The 5 parameters are, respectively:

  • the .yaml param file
  • the initial scale error magnitude
  • the translational error magnitude
  • the heading error magnitude
  • the ID number for storing the resulting transform

In this case, we are registering the third row of the soybean dataset with an initial scale error magnitude of 10%, an traslational error magnitude of 2.5 metres, an heading error magnitude of 5 degrees, and an ID of 2.


AgriColMap is licensed under the GPL2 License. However, some libraries are available under different license terms. See below.

The following parts are licensed under GPL3:


The following parts are licensed under GPL2:


AgriColMap is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the licenses for more details.


Please cite the following paper when using AgriColMap for your research:

  title={{A}gri{C}ol{M}ap: {A}erial-Ground Collaborative {3D} Mapping
         for Precision Farming},
  author={Potena, Ciro and Khanna, Raghav and Nieto, Juan and
          Siegwart, Roland and Nardi, Daniele and Pretto, Alberto},