OSM-BKI

To get started in Docker:

Make sure to download the example data using the python script in the example_data directory.

python example_data/download_data.py

After downloading the example data, build the ROS2 Humble docker container.

sudo bash/build_container.sh

Once it finishes building the container, set an environment variable pointing to the path of the example data.

export DATASETS_DIR=$PWD/example_data

This will point to the directory where you just downloaded sample data. Now you can run the docker container and it will have access to the example data.

bash docker/run_and_enter_container.sh

Once you're in the container, build the OSM-BKI package using colcon and source it like so:

cd bki_ws
colcon build

Source the workspace

source install/setup.bash

and launch the example.

ros2 launch osm_bki mcd_example_launch.py

Citation

OSM-BKI is an ongoing research project, but the results have been exiting and we want to extend that excitement to all who are interested. If would like to reference our project in your work, you can use the bibtex below:

Acknowledgment

We would like to extend our graditude to Professor Lu Gan for her coadvisement throughout this project and for the foundation on which this work has been built. To cite her original work, you can use the bibtex below:

@ARTICLE{gan2019bayesian,
author={L. {Gan} and R. {Zhang} and J. W. {Grizzle} and R. M. {Eustice} and M. {Ghaffari}},
journal={IEEE Robotics and Automation Letters},
title={Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping},
year={2020},
volume={5},
number={2},
pages={790-797},
keywords={Mapping;semantic scene understanding;range sensing;RGB-D perception},
doi={10.1109/LRA.2020.2965390},
ISSN={2377-3774},
month={April},}

as well as visit the repository for S-BKI:

https://github.com/ganlumomo/BKISemanticMapping