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AirLoop

This repo contains the source code for paper:

Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.

Watch on YouTube

Demo

Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:

Improved loop closure detection on TartanAir after extended training:

Usage

Dependencies

  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm

Data

We used the following subsets of datasets in our expriments:

  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.

The datasets are aranged as follows:

$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).

Configuration

The following values in config/config.yaml need to be set:

  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.

Commandline

The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:

$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>

Extra options*:

  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.

* See main_single.py for more settings.
** See main.py for defaults.

Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.

About

[ICRA 2022] Lifelong Loop Closure Detection

sairlab.org/airloop

Topics

lifelong-learningloop-closure-detection

Resources

Readme
BSD-3-Clause license
Activity
Custom properties

Stars

98 stars

Watchers

4 watching

Forks

6 forks
Report repository

Releases

Packages

Used by

Contributors

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try {
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var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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Skip to content
BranchesTags
Repository files navigation
AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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try {
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var __re = new RegExp('^' + ".*" + '

Skip to content
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AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
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Used by
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Skip to content
BranchesTags
Repository files navigation
AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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Skip to content
BranchesTags
Repository files navigation
AirLoop
This repo contains the source code for paper:
Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." International Conference on Robotics and Automation (ICRA), 2022.
Watch on YouTube
Demo
Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially:
Improved loop closure detection on TartanAir after extended training:
Usage
Dependencies
  • Python >= 3.5
  • PyTorch < 1.8
  • OpenCV >= 3.4
  • NumPy >= 1.19
  • Matplotlib
  • ConfigArgParse
  • PyYAML
  • tqdm
Data
We used the following subsets of datasets in our expriments:
  • TartanAir, download with tartanair_tools
    • Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
  • RobotCar, download with RobotCarDataset-Scraper
    • Train: 2014-11-28-12-07-13, 2014-12-10-18-10-50, 2014-12-16-09-14-09;
    • Test: 2014-06-24-14-47-45, 2014-12-05-15-42-07, 2014-12-16-18-44-24;
  • Nordland, download with gdown from Google Drive
    • Train/Test: All four seasons with recommended splits.
The datasets are aranged as follows:
$DATASET_ROOT/
├── tartanair/
│ ├── abandonedfactory_night/
| | ├── Easy/
| | | └── ...
│ │ └── Hard/
│ │ └── ...
│ └── ...
├── robotcar/
│ ├── train/
│ │ ├── 2014-11-28-12-07-13/
│ │ └── ...
│ └── test/
│ ├── 2014-06-24-14-47-45/
│ └── ...
└── nordland/
├── train/
│ ├── fall_images_train/
│ └── ...
└── test/
├── fall_images_test/
└── ...

Note: For TartanAir, only <ENVIRONMENT>/<DIFFICULTY>/<image|depth>_left.zip is required. After unziping downloaded zip files, make sure to remove the duplicate <ENVIRONMENT> directory level (tartanair/abandonedfactory/abandonedfactory/Easy/... -> tartanair/abandonedfactory/Easy/...).
Configuration
The following values in config/config.yaml need to be set:
  • dataset-root: The parent directory to all datasets ($DATASET_ROOT above);
  • catalog-dir: An (initially empty) directory for caching processed dataset index;
  • eval-gt-dir: An (initially empty) directory for groundtruth produced during evaluation.
Commandline
The following command trains the model with the specified method on TartanAir with default configuration and evaluate the performance:
$ python main.py --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>
Extra options*:
  • --dataset <tartanair/robotcar/nordland>: dataset to use.
  • --envs <LIST_OF_ENVIRONMENTS>: order of environments.**
  • --epochs <LIST_OF_EPOCHS>: number of epochs to train in each environment.**
  • --eval-save <PATH>: save path for predicted pairwise similarities generated during evaluation.
  • --out-dir <DIR>: output directory for model checkpoints and importance weights.
  • --log-dir <DIR>: Tensorboard logdir.
  • --skip-train: perform evaluation only.
  • --skip-eval: perform training only.
* See main_single.py for more settings.

** See main.py for defaults.
Evaluation results (R@100P in each environment) will be logged to console. --eval-save can be specified to save the predicted similarities in .npz format.
About
[ICRA 2022] Lifelong Loop Closure Detection
sairlab.org/airloop
Topics
lifelong-learningloop-closure-detection
Resources
Readme
BSD-3-Clause license
Activity
Custom properties
Stars
98 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages