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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

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