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3D Lidar-based marine object detection for USV
Received date: 2017-01-11
Online published: 2017-02-28
To realize autonomous navigation and avoidance, it is necessary for an unmanned surface vehicle (USV) to perceive the surrounding environment in real time, and detect obstacles threatening its sailing. Three dimensional Lidar plays an important role in unmanned system obstacle detection. In this paper, an obstacle detection algorithm based on 3D Lidar is proposed. The 3D laser point cloud is projected onto a 2.5D grid map in one period. The obstacles are clustered and segmented. The elliptical feature of obstacles is extracted from the raw laser point. Dynamic obstacles are found and tracked using the
nearest neighbor data association and a Kalman filter. The simulation data based on the electronic chart verifies effectiveness of the method in USV obstacle detection.
LI Xiaomao, ZHANG Xin, WANG Wentao, QU Dong, ZHU Chuan . 3D Lidar-based marine object detection for USV[J]. Journal of Shanghai University, 2017 , 23(1) : 27 -36 . DOI: 10.3969/j.issn.1007-2861.2016.07.022
[1] Liu Z, Zhang Y, Yu X, et al. Unmanned surface vehicles: an overview of developments and challenges [J]. Annual Reviews in Control, 2016, 41: 71-93.
[2] Wang H, Wei Z, Wang S, et al. Real-time obstacle detection for unmanned surface vehicle [C]// IEEE Defense Science Research Conference and Expo. 2011: 1-4.
[3] Wang H, Mou X, Mou W, et al. Vision based long range object detection and tracking for unmanned surface vehicle [C]// IEEE International Conference on Cybernetics and Intelligent Systems. 2015: 101-105.
[4] Li C, Cao Z, Xiao Y, et al. Fast object detection from unmanned surface vehicles via objectness and saliency [C]// IEEE Chinese Automation Congress. 2015: 500-505.
[5] Kristan M, Perš J, Stlic V, et al. A graphical model for rapid obstacle image-map estimation from unmanned surface vehicles [C]// ACCV. 2014: 391-406.
[6] Almeida C, Franco T, Ferreira H, et al. Radar based collision detection developments on USV ROAZ Ⅱ[C]// Oceans. 2009: 1-6.
[7] Blaich M, Schuster M, Reuter J. Collision avoidance for vessels using a low-cost radar sensor [C]// Proc of the Ifac World Congress. 2014: 9673-9678.
[8] Hermann D, Galeazzi R, Andersen J C, et al. Smart sensor based obstacle detection for highspeed unmanned surface vehicle [C]// Ifac Conference on Manoeuvring and Control of Marine Craft. 2015: 190-197.
[9] Peng Y, Qu D, Zhong Y, et al. The obstacle detection and obstacle avoidance algorithm based on 2-D lidar [C]// IEEE International Conference on Information and Automation. 2015: 1648-1653.
[10] Asvadi A, Premebida C, Peixoto P, et al. 3D lidar-based static and moving obstacle detection in driving environments [J]. Robotics & Autonomous Systems, 2016, 83(S1): 299-311.
[11] Halterman R, Bruch M. Velodyne hdl-64e lidar for unmanned surface vehicle obstacle detection[C]// SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2010: 76920D.
[12] Himmelsbach M, Müller A, Luettel T, et al. LIDAR-based 3D object perception [C]//Proceedings of 1st International Workshop on Cognition for Technical Systems. 2008: 1.
[13] Douillard B, Underwood J, Melkumyan N, et al. Hybrid elevation maps: 3D surface models for segmentation [C]// 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2010: 1532-1538.
[14] Asvadi A, Peixoto P, Nunes U. Detection and tracking of moving objects using 2.5D motion grids [C]// IEEE International Conference on Intelligent Transportation Systems. 2015: 788-793.
[15] 高红波, 王卫星. 一种二值图像连通区域标记的新算法[J]. 计算机应用, 2007, 27(11): 2776-2777.
[16] Cho H, Seo Y W, Kumar B V K V, et al. A multi-sensor fusion system for moving object detection and tracking in urban driving environments [C]// IEEE International Conference on Robotics and Automation. 2014: 1836-1843.
[17] 何友, 修建娟, 关欣. 雷达数据处理及应用[M]. 2 版. 北京: 电子工业出版社, 2009: 120-121.
[18] 辛煜, 梁华为, 梅涛, 等. 基于激光传感器的无人驾驶汽车动态障碍物检测及表示方法[J]. 机器人, 2014, 36(6): 654-661.
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