AirLoop: Lifelong Loop Closure Detection

Related tags

Deep Learning lifelong-learning loop-closure-detection
Overview

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).

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
    • 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 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 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>

--skip-train and --skip-eval can be specified to skip the train/test phase.

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Comments
  • 运行时提示无法识别的参数

    运行时提示无法识别的参数

    usage: main_single.py [-h] [--config CONFIG] [--task {train-seq,train-joint,eval}] [--catalog-dir CATALOG_DIR] [--no-parallel] [--devices DEVICES [DEVICES ...]] [--deterministic DETERMINISTIC] [--seed SEED] [--ll-config LL_CONFIG] [--dataset-root DATASET_ROOT] [--dataset DATASET] [--include INCLUDE] [--exclude EXCLUDE] [--scale SCALE] [--num-workers NUM_WORKERS] [--gd-dim GD_DIM] [--load LOAD] [--save SAVE] [--save-freq SAVE_FREQ] [--save-steps SAVE_STEPS [SAVE_STEPS ...]] [--ll-method LL_METHOD] [--ll-weight-dir LL_WEIGHT_DIR] [--ll-weight-load LL_WEIGHT_LOAD [LL_WEIGHT_LOAD ...]] [--ll-strength LL_STRENGTH [LL_STRENGTH ...]] [--batch-size BATCH_SIZE] [--lr LR] [--w-decay W_DECAY] [--epoch EPOCH] [--mem-size MEM_SIZE] [--log-dir LOG_DIR] [--viz-start VIZ_START] [--viz-freq VIZ_FREQ] [--eval-split-seed EVAL_SPLIT_SEED] [--eval-percentage EVAL_PERCENTAGE] [--eval-save EVAL_SAVE] [--eval-desc-save EVAL_DESC_SAVE] [--eval-gt-save EVAL_GT_SAVE] [--eval-gt-load EVAL_GT_LOAD] main_single.py: error: unrecognized arguments: --eval-gt-dir=/data/gt/ 麻烦帮我看看这个错误，谢谢

    opened by yxh-yxhyxh 0
  • 代码运行时报参数错误

    代码运行时报参数错误

    代码运行时，报main.py: error: the following arguments are required: --out-dir, --envs。其中，--out-dir我猜测应该是输出目录，我新建了一个输出目录引用后没有提示这个参数了。但是--envs这个参数表示什么呢？还需要麻烦您告知一下，我没看出来，谢谢！

    opened by yxh-yxhyxh 5
Owner
Chen Wang
I am engaged in delivering simple and efficient source code.
Chen Wang
GitHub
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