AirLoop

This repo contains the source code for paper:

Dasong Gao, Chen Wang, Sebastian Scherer. “AirLoop: Lifelong Loop Closure Detection.” arXiv preprint arXiv:2109.08975 (2021).

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
- Train/Test: abandonedfactory_night, carwelding, neighborhood, office2, westerndesert;
RobotCar
- 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
- Train/Test: All four seasons with recommended splits.

The datasets are aranged as follows:

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

Configuration

The following values in 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 will train a model sequentially (except for joint) in the specified envronments and evaluate the performance:

$ python main.py --dataset <tartanair/robotcar/nordland> --out-dir <OUT_DIR> --envs <LIST_OF_ENVIRONMENTS> --epochs <LIST_OF_EPOCHS> --method <finetune/si/ewc/kd/rkd/mas/rmas/airloop/joint>

AirLoop: Lifelong Loop Closure Detection

AirLoop

Demo