Air Series Articles Released

About Air Series Articles

  • It is a collection of articles mentored by Chen Wang.

  • All articles are first authored by junior researchers.

  • A wide variety of topics in robotics are covered.

Air Series Articles I

  • [1]
    AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration.
    Bowen Li, Chen Wang, Pranay Reddy, Seungchan Kim, Sebastian Scherer.
    European Conference on Computer Vision (ECCV), 2022.
  • [2]
    AirObject: A Temporally Evolving Graph Embedding for Object Identification.
    Nikhil Varma Keetha, Chen Wang, Yuheng Qiu, Kuan Xu, Sebastian Scherer.
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  • [3]
    AirLoop: Lifelong Loop Closure Detection.
    Dasong Gao, Chen Wang, Sebastian Scherer.
    International Conference on Robotics and Automation (ICRA), 2022.
  • [4]
    AirDOS: Dynamic SLAM benefits from Articulated Objects.
    Yuheng Qiu, Chen Wang, Wenshan Wang, Mina Henein, Sebastian Scherer.
    International Conference on Robotics and Automation (ICRA), 2022.
  • [5]
    AirCode: A Robust Object Encoding Method.
    Kuan Xu, Chen Wang, Chao Chen, Wei Wu, Sebastian Scherer.
    IEEE Robotics and Automation Letters (RA-L), 2022.
    Accepted to ICRA 2022

    • Air Series Articles II (To release)

  • [1]
    AirLoc: Object-based Indoor Relocalization.
    Aryan, Bowen Li, Sebastian Scherer, Yun-Jou Lin, Chen Wang.
    arXiv preprint arXiv:2304.00954, 2023.
  • [2]
    AirVO: An Illumination-Robust Point-Line Visual Odometry.
    Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, Lihua Xie.
    arXiv preprint arXiv:2212.07595, 2023.
  • [3]
    AirLine: Efficient Learnable Line Detection with Local Edge Voting.
    Xiao Lin, Chen Wang.
    arXiv preprint arXiv:2303.16500, 2023.

    • First Author Information (When work was done)

    • Bowen Li
      • Junior student at Tongji University, China.
      • Now: PhD student of CMU RI.
    • Nikhil Varma Keetha
      • Junior student at Indian Institute of Technology Dhanbad.
      • Now: Master student of CMU RI.
    • Dasong Gao
      • Master student at Carnegie Mellon University.
      • Now: PhD student of MIT EECS.
    • Yuheng Qiu
      • Undergraduate of Chinese University of Hong Kong.
      • Now: PhD student of CMU ME.
    • Kuan Xu
      • Master Graduate of Harbin Institute of Technology, China.
      • Now: PhD student of NTU EEE.
    • Xiao Lin
      • Freshman at Georgia Institute of Technology.
      • Now: Sophomore at Georgia Institute of Technology.
    • Aryan
      • Junior student at Delhi Technological University.
      • Now: Master student of CMU RI.

    Contribution

    • AirDet: Few-shot Detection without Fine-tunning

      • The first practical few-shot object detection method that requires no fine-tunning.
      • It achieves even better results than the exhaustively fine-tuned methods (up to 60% improvements).
      • Validated on real world sequences from DARPA Subterranean (SubT) challenge.
    Only three examples are given for novel object detection without fine-tunning.

    • AirObject: Temporal Object Embedding

      • The first temporal object embedding method.
      • It achieves the state-of-the-art performance for video object identification.
      • Robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform.
      • Project Page: https://sairlab.org/airobject
    Temporal object matching on videos.

    • AirDOS: Dynamic Object-aware SLAM (DOS) system

    Dynamic Objects can correct the camera pose estimation.
    The model is able to correct previously made mistakes after learning more environments.

    • AirCode: Robust Object Encoding

      • The first deep point-based object encoding for single image.
      • It achieves the state-of-the-art performance for object re-identification.
      • Robust to viewpoint shift, object deformation, and scale transform.
      • Project Page: https://sairlab.org/aircode
    A human matching demo.

    More information can be found at the research page.

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