Air Series Articles Prereleased

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.
    By Li, B., Wang, C. and Scherer, S.
    In arXiv preprint arXiv:2112.017402021.

  • [2]
    AirObject: A Temporally Evolving Graph Embedding for Object Identification.
    By Keetha, N.V., Wang, C., Qiu, Y., Xu, K. and Scherer, S.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2022.

  • [3]
    AirLoop: Lifelong Loop Closure Detection.
    By Gao, D., Wang, C. and Scherer, S.
    In International Conference on Robotics and Automation (ICRA)2022.

  • [4]
    AirDOS: Visual SLAM Benefits from Dynamic Objects.
    By Qiu, Y., Wang, C., Wang, W., Henein, M. and Scherer, S.
    In International Conference on Robotics and Automation (ICRA)2022.

  • [5]
    AirCode: A Robust Object Encoding Method.
    By Xu, K., Wang, C., Chen, C., Wu, W. and Sebastian, S.
    In IEEE Robotics and Automation Letters (RA-L), 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.
    Temporal object matching on videos.
    • AirDOS: Dynamic Object-aware SLAM (DOS) system

      • The first DOS system showing that camera pose estimation can be improved by incorporating dynamic articulated objects.
      • Establish 4-D dynamic object maps.
    Dynamic Objects can correct the camera pose estimation.
    • AirLoop: Lifelong Learning for Robots

      • The first lifelong learning method for loop closure detection.
      • Model incremental improvement even after deployment.
    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.
    A human matching demo.

    Congratulations to the above young researchers!

    More information can be found at the research page.

    Some project pages will be released soon.