AirCode

Xu, Kuan, Chen Wang, Chao Chen, Wei Wu, and Sebastian Scherer. ""AirCode: A Robust Object Encoding Method"." IEEE Robotics and Automation Letters (2022). (Accepted to ICRA 2022)

Demo

Object matching comparison when the objects are non-rigid and the view is changed, left is the result of our method while right is the result of NetVLAD

Relocalization on KITTI datasets

Dependencies

• Python 3.7
• Torchvision 0.8.0
• PyTorch 1.7.0
• OpenCV 4.4.0
• Matplotlib 3.3.3
• NumPy 1.19.2
• Pyyaml 5.3.1

Data

Four datasets are used in our experiments.

KITTI Odometry

For relocalization experiment. Three sequences are selected, and they are "00", "05" and "06".

KITTI Tracking

For multi-object matching experiment. Four sequences are selected, and they are "0002", "0003", "0006", "0010".

VOT Datasets

For single-object matching experiment. We select three sequences from VOT2019 datasets and they are "bluecar", "bus6" and "humans_corridor_occ_2_A", because the tracked objects in these sequences are included in coco datasets, which are the data we used to train mask-rcnn.

OTB Datasets

For single-object matching experiment. We select five sequences and they are "BlurBody", "BlurCar2", "Human2", "Human7" and "Liquor".

Examples

Relocalization on KITTI Datasets

1. Extract object descrptors

   python experiments/place_recogination/online_relocalization.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_MIDDLE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS
   

2. Compute precision-recall curves

   python experiments/place_recogination/offline_process.py -c config/experiment_place_recognization.yaml -d PATH_TO_DATASET -n PATH_TO_MIDDLE_RESULTS -s PATH_TO_SAVE_RESULTS
   

3. Compute top-K relocalization results

   python experiments/place_recogination/offline_topK.py -c config/experiment_place_recognization.yaml -d PATH_TO_DATASET -n PATH_TO_MIDDLE_RESULTS -s PATH_TO_SAVE_RESULTS
   

Object Matching on OTB, VOT or KITTI Tracking Datasets

• Run multi-object matching experiment in KITTI Tracking Datasets Modify the config file and run

  python experiments/object_tracking/object_tracking.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS 
  

• Run single-object matching experiment in OTB or VOT Datasets Modify the config file and run

  python experiments/object_tracking/single_object_tracking.py -c config/experiment_tracking.yaml -g 1 -s PATH_TO_SAVE_RESULTS -d PATH_TO_DATASET -m PATH_TO_MODELS 
  

Pretrained Models

• Go to this link.