gira3d/gira3d-registration

Frame-to-Frame Registration using Gaussian Mixture Models.

GIRA Registration

Overview

This repository is a colcon workspace. You can learn more about colcon here.

The wet/src folder contains git submodules that contain the main codebase for this work. The dry/src folder contains git submodules that download and locally install third party packages required for this work.

1. gmm_d2d_registration: C++ implementation of the distribution-to-distribution GMM registration.
2. gmm_d2d_registration_matlab: MATLAB implementation of the distribution-to-distribution GMM registration as well as MEX files.
3. gmm_d2d_registration_py: Python bindings over the C++ implementation.

Quick Start

Installation

git clone [email protected]:gira3d/gira3d-registration.git --recursive
cd gira3d-registration
python3.8 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install numpy colcon-common-extensions
sudo apt install cmake libboost-all-dev libblas-dev
./build.sh

Once the sandbox is built you will also need to install the following:

pip install tqdm matplotlib open3d

Ubuntu 18.04 additionally requires that you install the following for plotting:

sudo apt install python3-tk

Documentation and Examples

We use MkDocs to document this codebase. To install and run documentation, please do the following:

cd /path/to/gira3d-registration
source workon
pip install mkdocs mkdocs-material
mkdocs serve

Now open a web browser and go to localhost to view the documentation. Documentation for tutorials and examples that use one or more of the submodules is provided.

Operating Systems

This sandbox was tested on the following operating systems

• Ubuntu 20.04
• Ubuntu 18.04

References

If you use this work in your own research, please cite the following publications:

1. W. Tabib, C. O’Meadhra, and N. Michael, “On-Manifold GMM Registration,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3805–3812, Oct. 2018, doi: 10.1109/LRA.2018.2856279.
2. W. Tabib and N. Michael, “Simultaneous Localization and Mapping of Subterranean Voids with Gaussian Mixture Models,” in Field and Service Robotics, Singapore, 2021, pp. 173–187. doi: 10.1007/978-981-15-9460-1_13.