Air Series Articles Released

Air Series is a collection of articles that are first authored by junior researchers.

This series focuses on a wide variety of topics in robot perception.

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. AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System.
    Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, Lihua Xie.
    arXiv preprint arXiv:2408.03520, 2024.
    SAIR Lab Recommended
  2. AirShot: Efficient Few-Shot Detection for Autonomous Exploration.
    Zihan Wang, Bowen Li, Chen Wang, Sebastian Scherer.
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
  3. AirIMU: Learning Uncertainty Propagation for Inertial Odometry.
    Yuheng Qiu, Chen Wang, Xunfei Zhou, Youjie Xia, Sebastian Scherer.
    arXiv preprint arXiv:2310.04874, 2024.
  4. AirLoc: Object-based Indoor Relocalization.
    Aryan, Bowen Li, Sebastian Scherer, Yun-Jou Lin, Chen Wang.
    arXiv preprint arXiv:2304.00954, 2024.
  5. AirVO: An Illumination-Robust Point-Line Visual Odometry.
    Kuan Xu, Yuefan Hao, Shenghai Yuan, Chen Wang, Lihua Xie.
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
    SAIR Lab Recommended
  6. AirLine: Efficient Learnable Line Detection with Local Edge Voting.
    Xiao Lin, Chen Wang.
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 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.
  • Zihan Wang
    • Junior student at University of Edinburgh.
    • 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.