Long-range UAV Thermal Geo-localization with Satellite Imagery

被引:2
作者
Xiao, Jiuhong [1 ]
Tortei, Daniel [2 ]
Roura, Eloy [2 ]
Loianno, Giuseppe [1 ]
机构
[1] NYU, Tandon Sch Engn, Brooklyn, NY 11201 USA
[2] Technol Innovat Inst, Autonomous Robot Res Ctr, Abu Dhabi, U Arab Emirates
来源
2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2023年
关键词
DOMAIN ADAPTATION;
D O I
10.1109/IROS55552.2023.10342068
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Onboard sensors, such as cameras and thermal sensors, have emerged as effective alternatives to Global Positioning System (GPS) for geo-localization in Unmanned Aerial Vehicle (UAV) navigation. Since GPS can suffer from signal loss and spoofing problems, researchers have explored camera-based techniques such as Visual Geo-localization (VG) using satellite RGB imagery. Additionally, thermal geo-localization (TG) has become crucial for long-range UAV flights in low-illumination environments. This paper proposes a novel thermal geo-localization framework using satellite RGB imagery, which includes multiple domain adaptation methods to address the limited availability of paired thermal and satellite images. The experimental results demonstrate the effectiveness of the proposed approach in achieving reliable thermal geo-localization performance, even in thermal images with indistinct self-similar features. We evaluate our approach on real data collected onboard a UAV. We also release the code and Boson-nighttime, a dataset of paired satellite-thermal and unpaired satellite images for thermal geo-localization with satellite imagery. To the best of our knowledge, this work is the first to propose a thermal geo-localization method using satellite RGB imagery in long-range flights.
引用
收藏
页码:5820 / 5827
页数:8
相关论文
共 42 条
  • [31] Deep learning
    Rusk, Nicole
    [J]. NATURE METHODS, 2016, 13 (01) : 35 - 35
  • [32] Shan M, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), P114, DOI 10.1109/ROBIO.2015.7418753
  • [33] Shetty A, 2019, IEEE INT CONF ROBOT, P1827, DOI [10.1109/ICRA.2019.8794228, 10.1109/icra.2019.8794228]
  • [34] Video Google: A text retrieval approach to object matching in videos
    Sivic, J
    Zisserman, A
    [J]. NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, 2003, : 1470 - +
  • [35] UAV-Satellite View Synthesis for Cross-View Geo-Localization
    Tian, Xiaoyang
    Shao, Jie
    Ouyang, Deqiang
    Shen, Heng Tao
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 32 (07) : 4804 - 4815
  • [36] Adversarial Discriminative Domain Adaptation
    Tzeng, Eric
    Hoffman, Judy
    Saenko, Kate
    Darrell, Trevor
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 2962 - 2971
  • [37] Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning
    Vs, Vibashan
    Poster, Domenick
    You, Suya
    Hu, Shuowen
    Patel, Vishal M.
    [J]. 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022), 2022, : 3697 - 3706
  • [38] Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition
    Warburg, Frederik
    Hauberg, Soren
    Lopez-Antequera, Manuel
    Gargallo, Pau
    Kuang, Yubin
    Civera, Javier
    [J]. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, : 2623 - 2632
  • [39] Monocular Vision for Long-term Micro Aerial Vehicle State Estimation: A Compendium
    Weiss, Stephan
    Achtelik, Markus W.
    Lynen, Simon
    Achtelik, Michael C.
    Kneip, Laurent
    Chli, Margarita
    Siegwart, Roland
    [J]. JOURNAL OF FIELD ROBOTICS, 2013, 30 (05) : 803 - 831
  • [40] Yixiao Ge, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12349), P369, DOI 10.1007/978-3-030-58548-8_22