Air Series Articles Released

Air Series is a collection of articles mentored by Chen Wang.

A wide variety of topics in robotics are covered, including localization, detection, and lifelong learning.

All articles are first authored by Undergraduate or Master students and second authored by Chen Wang.

Air Series Articles

  • [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
  • First Author Information (When work was done)

    • Bowen Li
      • RISS intern at Carnegie Mellon University.
      • Junior student at Tongji University, China.
      • Now: Incoming PhD student of CMU RI.
    • Nikhil Varma Keetha
      • RISS intern at Carnegie Mellon University.
      • Junior student at Indian Institute of Technology Dhanbad.
      • Now: Incoming Master student of CMU RI.
    • Dasong Gao
      • Master student at Carnegie Mellon University.
      • Now: Incoming 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.
      • Engineer at Tencent and Geekplus.
      • Now: Incoming PhD student of NTU EEE.

    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://chenwang.site/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://chenwang.site/aircode
    A human matching demo.

    Congratulations to the above young researchers!

    More information can be found at the research page.