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Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot

This repository contains the overwhelming majority of the work done during my master's thesis.

Important

To clone, it is recommended to install git-lfs on your system.

Abstract

This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.

Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching. Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment. This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.

Content

  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a dedicated repository (the list will be made available here).

Build latex & run scripts

See the docker/README.md for more information about the dependencies and scripts/README.md for the scripts.

Acknowledgements

This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems at the Danmarks Tekniske Universitet. It took place at the U2IS lab of ENSTA Paris.

It was supervised by Søren HANSEN and co-supervised by Alexandre CHAPOUTOT and Thibault TORALBA.

License

Given that this repository contains multiple type of document, two licenses are used:

  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in logo, are under CC BY-SA 4.0 license. The templates are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.

If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.

About

Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot

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Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
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Readme
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Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
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Report repository
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Used by
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Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
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Report repository
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Used by
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Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
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Used by
Contributors
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Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
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GitHub - Seb-sti1/mastersthesis: Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot · GitHub
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Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
0 forks
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GitHub - Seb-sti1/mastersthesis: Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot · GitHub
Skip to content
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Latest commit
History
146 Commits
Folders and files
NameName
Last commit message
Last commit date
datasets
datasets
docker
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latex
latex
notes
notes
scripts
scripts
.gitattributes
.gitattributes
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
This repository contains the overwhelming majority of the work done during my master's
thesis.
Important
To clone, it is recommended to install git-lfs on your system.
Abstract
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured environments.
The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a topological map with identified paths, intersections, and potential targets.
The terrestrial robot receives high-level instructions (e.g., “reach a target”) and navigate using this topological map.
It utilizes onboard sensors to match environmental features previously detected with the drone and associated to the topological map.
Techniques are proposed to handle the perspective differences between aerial and ground views, including bird's-eye view generation and sparse correspondence matching.
Simulated real-world scenario demonstrates that the proposed system offers a promising foundation for real-world deployment.
This project explores the collaboration between an aerial drone and a terrestrial robot for navigating unstructured
environments. The drone performs initial environmental mapping using sensors (e.g. GNSS, cameras...), generating a
topological map with identified paths, intersections, and potential targets. The terrestrial robot receives high-level
instructions (e.g., “reach a target”) and navigate using this topological map. It utilizes onboard sensors to match
environmental features previously detected with the drone and associated to the topological map.
Content
  • Datasets: An empty folder where the datasets should be stored. The list and links to the datasets used are
specified in the dedicated README.md.
  • Docker: Dockerfiles to simplify reproducibility.
  • Latex: The latex source files for the project plan, report and defence.
  • Notes: The notes of my research and intermediate results. See dedicated README.md.
  • Scripts: Scripts to test/automate things. If some script become more than a test, it will be moved to a
dedicated repository (the list will be made available here).
Build latex & run scripts
See the docker/README.md for more information about the dependencies
and scripts/README.md for the scripts.
Acknowledgements
This is the source files of my Master's thesis for my M.Sc. Eng in Autonomous Systems
at the Danmarks Tekniske Universitet. It took place at
the U2IS lab of ENSTA Paris.
It was supervised by Søren HANSEN and co-supervised
by Alexandre CHAPOUTOT
and Thibault TORALBA.
License
Given that this repository contains multiple type of document, two licenses are used:
  • The files in scripts and docker (mostly python and dockerfile) are under GNU GPL
license.
  • The files in latex and notes (mostly images, markdown, latex files), except for images in
logo, are under CC BY-SA 4.0 license. The templates
are largely based of DTU's templates.
  • The images in logo are copyrighted, belong to their rightful owner and should be used only when
permitted by law.
  • No datasets will be stored in datasets, please refer to the license given by the author of the dataset.
If you have any doubt regarding the licensing of part of this repository, please consider submitting an issue.
About
Collaborative navigation in unstructured environments using an aerial drone and a terrestrial robot
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
0 forks
Report repository
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Used by
Contributors
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