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CoarseHash

Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]

Generating Pseudo Localization Datasets

1. Download Ingredient Datasets

Deep1B

The dataset is available here for download. A download script is provided by the original authors.

We only use the descriptors within the base/base00 file, so you may only want to download that single file.

FAS100K

The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.

The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.

2. Generate 20K, 1M, and 10M

Prerequisites

numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6

Run

Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.

License

The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.

If you find this repository useful or use these datasets, cite:

Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.

bibtex:

@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects

Delta Descriptors (2020)

seq2single (2019)

LoST (2018)

About

Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations

Topics

datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics

Resources

Readme
Activity

Stars

17 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
Resources
Readme
Activity
Stars
17 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
Resources
Readme
Activity
Stars
17 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
Resources
Readme
Activity
Stars
17 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
Resources
Readme
Activity
Stars
17 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
Resources
Readme
Activity
Stars
17 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

Related Projects
Delta Descriptors (2020)
seq2single (2019)
LoST (2018)
About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Topics
datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
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CoarseHash
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations [arxiv][IEEE Xplore][YouTube]
Generating Pseudo Localization Datasets
1. Download Ingredient Datasets
Deep1B
The dataset is available here for download. A download script is provided by the original authors.
We only use the descriptors within the base/base00 file, so you may only want to download that single file.
FAS100K
The dataset is described in the paper and its NetVLAD descriptors for both the reference and the query set are available for download here.
The downloaded .npz comprises 7 ndarrays named arr_0 to arr_6, comprising respectively reference descriptors, query descriptors, ground truth match indices for query data, reference poses (xyz), query poses (xyz), ignore, ignore.
2. Generate 20K, 1M, and 10M
Prerequisites
numpy
scikit_learn

See requirements.txt, generated using pipreqs==0.4.10 and python3.5.6
Run
Set the path and dataset variable ("20K", "1M" or "10M") and run python preProcData.py to generate the localization dataset. The "10M" dataset can take around 25 GB of RAM when performing PCA. A low-memory alternative would be to use Incremental PCA.
License
The code is released under MIT License. FAS100K license is as specified on the download link. For Deep1B, refer to its original sources as mentioned above.
If you find this repository useful or use these datasets, cite:
Garg, Sourav, and Michael Milford. "Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations." In 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
bibtex:
@inproceedings{garg2020fast,
title={Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations},
author={Garg, Sourav and Milford, Michael},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2020}
}

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About
Benchmark datasets used in ICRA 2020 paper: Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
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datasetdeep-learninghashinglarge-scalelocalizationplace-recognitionquantizationrobotics
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