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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.