Volume 44 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
LIU Feng, WANG Zan, WANG Xiangjun. Obstacles detection method for UAV based on monocular vision and laser projection[J]. Journal of Applied Optics, 2023, 44(1): 202-210. doi: 10.5768/JAO202344.0107002
Citation: LIU Feng, WANG Zan, WANG Xiangjun. Obstacles detection method for UAV based on monocular vision and laser projection[J]. Journal of Applied Optics, 2023, 44(1): 202-210. doi: 10.5768/JAO202344.0107002

Obstacles detection method for UAV based on monocular vision and laser projection

doi: 10.5768/JAO202344.0107002
  • Received Date: 2022-05-10
  • Rev Recd Date: 2022-06-13
  • Available Online: 2022-11-23
  • Publish Date: 2023-01-17
  • In order to meet the requirement of active obstacle avoidance of microminiature unmanned aerial vehicle (UAV) in flight mission, an obstacles detection method based on monocular vision and active laser lattice projection of microminiature UAV for obstacles avoidance was proposed. The projected laser lattice patterns were collected by a monocular camera, and through the processes of image segmentation, clustering and centroid extraction, the ambiguity of the characteristic consistent laser point was quickly eliminated by the constraint of the laser line equation of the image plane. The laser points were used to detect the distribution of obstacles in the front space of the UAV. The experimental results show that the relative error of obstacles detection is within 1.5% when the baseline distance is 65 mm and the working distance is 7 m. The proposed method has high accuracy and low time complexity, and can meet the requirements for obstacles detection methods of microminiature UAV with low computing power, which provides the data support for the generation of further obstacles avoidance strategies.
  • loading
  • [1]
    HU Tianyu, SUN Xiliang, SU Yanjun, et al. Development and performance evaluation of a very low-cost UAV-lidar system for forestry applications[J]. Remote Sensing,2020,13(1):77. doi: 10.3390/rs13010077
    FAN Xuanmei, XU Qiang, ALONSO-RODRIGUEZ A, et al. Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response[J]. Landslides,2019,16(5):1003-1020. doi: 10.1007/s10346-019-01159-x
    MAES W H, STEPPE K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture[J]. Trends in Plant Science,2019,24(2):152-164. doi: 10.1016/j.tplants.2018.11.007
    MENÉNDEZ O, PÉREZ M, AUAT CHEEIN F. Visual-based positioning of aerial maintenance platforms on overhead transmission lines[J]. Applied Sciences,2019,9(1):165. doi: 10.3390/app9010165
    WANG Jingjing, JIANG Chunxiao, HAN Zhu, et al. Taking drones to the next level: cooperative distributed unmanned-aerial-vehicular networks for small and mini drones[J]. IEEE Vehicular Technology Magazine,2017,12(3):73-82. doi: 10.1109/MVT.2016.2645481
    ZHANG Xuejun, DU Yanshuang, GU Bo, et al. Survey of safety management approaches to unmanned aerial vehicles and enabling technologies[J]. Journal of Communications and Information Networks,2018,3(4):1-14. doi: 10.1007/s41650-018-0038-x
    王海群, 王水满, 张怡. 基于激光雷达信息的无人机避障控制研究[J]. 激光杂志,2019,40(12):76-79. doi: 10.14016/j.cnki.jgzz.2019.12.076

    WANG Haiqun, WANG Shuiman, ZHANG Yi. Control of UAV barrier avoidance based on lidar information[J]. Laser Journal,2019,40(12):76-79. doi: 10.14016/j.cnki.jgzz.2019.12.076
    吴开华, 王文杰. 植保无人机结构光视觉的障碍物检测方法[J]. 光电工程,2018,45(4):32-40.

    WU Kaihua, WANG Wenjie. Detection method of obstacle for plant protection UAV based on structured light vision[J]. Opto-Electronic Engineering,2018,45(4):32-40.
    高天禹, 马雨婷, 韩成哲, 等. 基于激光测距与单目视觉的微型无人机室内目标人物搜索方法研究[J]. 中国科学:技术科学,2020,50(7):971-982. doi: 10.1360/SST-2020-0130

    GAO Tianyu, MA Yuting, HAN Chengzhe, et al. Applying a micro-UAV for searching an indoor target person using laser ranging and monocular vision[J]. Scientia Sinica (Technologica),2020,50(7):971-982. doi: 10.1360/SST-2020-0130
    寇展, 吴健发, 王宏伦, 等. 基于深度学习的低空小型无人机障碍物视觉感知[J]. 中国科学:信息科学,2020,50(5):692-703. doi: 10.1360/N112019-00034

    KOU Zhan, WU Jianfa, WANG Honglun, et al. Obstacle visual sensing based on deep learning for low-altitude small unmanned aerial vehicles[J]. Scientia Sinica (Informationis),2020,50(5):692-703. doi: 10.1360/N112019-00034
    KESELMAN L, WOODFILL J I, GRUNNET-JEPSEN A, et al. Intel(R) RealSense(TM) stereoscopic depth cameras[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, HI, USA: IEEE, 2017: 1267-1276.
    AZETSU T, SUETAKE N. Hue-preserving image enhancement in CIELAB color space considering color gamut[J]. Optical Review,2019,26(2):283-294. doi: 10.1007/s10043-019-00499-2
    ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334. doi: 10.1109/34.888718
    包建强, 张献州, 李圆, 等. 多种空间直线拟合方法应用分析[J]. 测绘科学,2020,45(5):132-139. doi: 10.16251/j.cnki.1009-2307.2020.05.020

    BAO Jianqiang, ZHANG Xianzhou, LI Yuan, et al. Applied analysis of various space linear fitting methods[J]. Science of Surveying and Mapping,2020,45(5):132-139. doi: 10.16251/j.cnki.1009-2307.2020.05.020
    LI Qianwen, WEI Zhihua, ZHAO Cairong. Optimized automatic seeded region growing algorithm with application to ROI extraction[J]. International Journal of Image and Graphics,2017,17(4):1750024. doi: 10.1142/S0219467817500243
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views (60) PDF downloads(17) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint