收稿日期: 2017-01-11
网络出版日期: 2017-02-28
基金资助
国家自然科学基金资助项目(61673254); 上海市自然科学基金资助项目(13ZR1454300); 上海市科委能力建设资助项目(14500500400)
3D Lidar-based marine object detection for USV
Received date: 2017-01-11
Online published: 2017-02-28
无人水面艇(unmanned surface vehicle, USV) 实现自主导航避障需要实时感知周围的环境信息, 检测出威胁无人水面艇航行的障碍物, 而3D激光雷达在无人系统障碍检测中起重要作用. 提出了一种基于3D激光雷达的障碍检测算法, 即将一个周期内的3D激光点云投影到2.5D栅格地图中, 对障碍物进行聚类分割；对栅格中的原始点云数据进行特征提取, 表示为椭圆障碍物. 而多帧激光数据采用最近邻数据关联识别出其中的动态障碍物, 并用卡尔曼滤波实时跟踪. 基于电子海图的仿真激光数据验证了该方法在无人水面艇障碍检测中的有效性.
李小毛, 张鑫, 王文涛, 瞿栋, 祝川 . 基于3D激光雷达的无人水面艇海上目标检测[J]. 上海大学学报(自然科学版), 2017 , 23(1) : 27 -36 . DOI: 10.3969/j.issn.1007-2861.2016.07.022
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.
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