Chinese Journal of Lasers, Volume. 47, Issue 1, 0110002(2020)

Adaptive Grid Representation Method for Unmanned Surface Vehicle Obstacle Based on Three-Dimensional Lidar

Deqing Liu*, Jie Zhang, and Jiucai Jin*
Author Affiliations
  • First Institute of Oceanography, Ministry of Natural Resources, Qingdao, Shandong 266061, China
  • show less
    References(16)

    [1] Liu Z X, Zhang Y M, Yu X et al. Unmanned surface vehicles: an overview of developments and challenges[J]. Annual Reviews in Control, 41, 71-93(2016).

    [3] Almeida C, Franco T, Ferreira H et al. Radar based collision detection developments on USV ROAZ II. [C]∥OCEANS 2009-EUROPE, May 11-14, 2009, Bremen, Germany. New York: IEEE, 10915094(2009).

    [4] Zhuang J Y, Zhang L, Zhao S Q et al. Radar-based collision avoidance for unmanned surface vehicles[J]. China Ocean Engineering, 30, 867-883(2016).

    [5] Ma Z L, Wen J, Liang X M. Video image clarity algorithm research of USV visual system under the sea fog[M]. ∥Tan Y, Shi Y, Mo H. Advances in swarm intelligence. Lecture notes in computer science. Berlin, Heidelberg: Springer, 7929, 436-444(2013).

    [6] Mou X Z, Wang H. Image-based maritime obstacle detection using global sparsity potentials[J]. Journal of Information and Communication Convergence Engineering, 14, 129-135(2016).

    [7] Polvara R, Sharma S, Wan J et al. Obstacle avoidance approaches for autonomous navigation of unmanned surface vehicles[J]. Journal of Navigation, 71, 241-256(2018).

    [8] Halterman R, Bruch M. Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection[J]. Proceedings of SPIE, 7692, 76920D(2010).

    [9] Lee S J, Moon Y S, Ko N Y et al. A method for object detection using point cloud measurement in the sea environment. [C]∥2017 IEEE Underwater Technology (UT), February 21-24, 2017, Busan, Korea. New York: IEEE, 16790560(2017).

    [10] Esposito J M, Graves M. An algorithm to identify docking locations for autonomous surface vessels from 3-D LiDAR scans. [C]∥2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), April 14-15, 2014, Woburn, MA, USA. New York: IEEE, 14501878(2014).

    [11] Wu P, Xie S R, Liu H L et al. Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring[J]. Industrial Robot: an International Journal, 44, 64-74(2017).

    [12] Wang S W, Xie L, Ma F et al. Research of obstacle recognition method for USV based on laser radar. [C]∥2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, AB, Canada. New York: IEEE, 343-348(2017).

    [14] Börcs A, Nagy B, Benedek C. Fast 3-D urban object detection on streaming point clouds[M]. ∥Agapito L, Bronstein M, Rother C. Computer vision-ECCV 2014 Workshops. Lecture notes in computer science. Cham: Springer, 8926, 628-639(2015).

    [15] Thompson D, Coyle E, Brown J. Efficient LiDAR-based object segmentation and mapping for maritime environments[J]. IEEE Journal of Oceanic Engineering, 44, 352-362(2019).

    [16] Li X M, Zhang X, Wang W T et al. 3D lidar-based marine object detection for USV[J]. Journal of Shanghai University(Natural Science Edition), 23, 27-36(2017).

    Tools

    Get Citation

    Copy Citation Text

    Deqing Liu, Jie Zhang, Jiucai Jin. Adaptive Grid Representation Method for Unmanned Surface Vehicle Obstacle Based on Three-Dimensional Lidar[J]. Chinese Journal of Lasers, 2020, 47(1): 0110002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: remote sensing and sensor

    Received: Jul. 22, 2019

    Accepted: Sep. 26, 2019

    Published Online: Jan. 9, 2020

    The Author Email: Deqing Liu (liudeqing@fio.org.cn), Jiucai Jin (liudeqing@fio.org.cn)

    DOI:10.3788/CJL202047.0110002

    Topics