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Overlap-based 3D LiDAR Monte Carlo Localization

This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.

It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.

Developed by Xieyuanli Chen and Thomas Läbe.

Localization results of overlap-based Monte Carlo Localization.

Publication

If you use our implementation in your academic work, please cite the corresponding paper:

@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies

We are using standalone Keras with a TensorFlow backend as a library for neural networks.

The code was tested with Ubuntu 18.04 with its standard python version 3.6.

In order to do training and testing on a whole dataset, you need an Nvidia GPU.

To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!

  • CUDA Installation guide: link

  • System dependencies:

    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip

  • Python dependencies (may also work with different versions than mentioned in the requirements file)

    sudo -H pip3 install -r requirements.txt

  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:

    git clone https://github.com/PRBonn/OverlapNet

How to use

Quick use

For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:

cd src/
python3 main_overlap_mcl.py

One could then get the online visualization of overlap-based MCL as shown in the gif.

More detailed usage

For more details about the usage and each module of this implementation, one could find them in MCL README.md.

Train a new observation model

To train a new observation model, one could find more information in prepare_training README.md.

Collection of preprocessed data

License

Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.

This project is free software made available under the MIT License. For details see the LICENSE file.

About

chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.

Topics

learninglidarlocalizationmclmonte-carlo

Resources

Readme
MIT license
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272 stars

Watchers

19 watching

Forks

68 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
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272 stars
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68 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
Activity
Custom properties
Stars
272 stars
Watchers
19 watching
Forks
68 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
Activity
Custom properties
Stars
272 stars
Watchers
19 watching
Forks
68 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
Activity
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68 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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Repository files navigation
Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
Activity
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Stars
272 stars
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68 forks
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try {
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Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
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MIT license
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272 stars
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Repository files navigation
Overlap-based 3D LiDAR Monte Carlo Localization
This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
It uses the OverlapNet to train an observation model for Monte Carlo Localization and achieves global localization with 3D LiDAR scans.
Developed by Xieyuanli Chen and Thomas Läbe.
Localization results of overlap-based Monte Carlo Localization.
Publication
If you use our implementation in your academic work, please cite the corresponding paper:
@inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
}

Dependencies
We are using standalone Keras with a TensorFlow backend as a library for neural networks.
The code was tested with Ubuntu 18.04 with its standard python version 3.6.
In order to do training and testing on a whole dataset, you need an Nvidia GPU.
To use a GPU, first you need to install the Nvidia driver and CUDA, so have fun!
  • CUDA Installation guide: link
  • System dependencies:
    sudo apt-get update sudo apt-get install -y python3-pip python3-tk
sudo -H pip3 install --upgrade pip
  • Python dependencies (may also work with different versions than mentioned in the requirements file)
    sudo -H pip3 install -r requirements.txt
  • OverlapNet: To use this implementation, one needs to clone OverlapNet to the local folder by:
    git clone https://github.com/PRBonn/OverlapNet
How to use
Quick use
For a quick demo, one could download the feature volumes and pre-trained sensor model, extract the feature volumes in the /data folder following the recommended data structure, and then run:
cd src/
python3 main_overlap_mcl.py
One could then get the online visualization of overlap-based MCL as shown in the gif.
More detailed usage
For more details about the usage and each module of this implementation, one could find them in MCL README.md.
Train a new observation model
To train a new observation model, one could find more information in prepare_training README.md.
Collection of preprocessed data
License
Copyright 2020, Xieyuanli Chen, Thomas Läbe, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
About
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Topics
learninglidarlocalizationmclmonte-carlo
Resources
Readme
MIT license
Activity
Custom properties
Stars
272 stars
Watchers
19 watching
Forks
68 forks
Report repository
Releases
Packages
Used by
Contributors
Languages