融合视觉与激光雷达，实现实时海上障碍物检测与定位
USV Obstacles Detection and Tracking in Marine Environments
- 基于相机与激光雷达融合，实现图像平面到3D点云的障碍物定位
- 在MIT海洋数据集上验证系统在多种海况下的实时性能
- 提出混合方案，构建更全面的周围环境障碍物地图
针对无人水面艇（USV）在海洋环境中实现稳健有效的障碍物检测与跟踪系统仍具挑战性。格拉尔实验室此前已开发出一种方法，可在图像平面上检测并跟踪障碍物，并将其定位至3D激光雷达点云中。本文在此基础上，首先在近期发布的海洋数据集上评估该系统的性能；随后将系统各模块集成至ROS平台，利用MIT海洋数据集中同步采集的激光雷达与相机数据，在多种海洋条件下进行实时测试。通过两种方法对比分析：一种结合相机与激光雷达进行融合检测与跟踪，另一种仅使用激光雷达点云。最终提出一种混合方法，融合两者优势，生成对USV周围环境更具信息量的障碍物地图。
原文摘要 · Abstract (English)
Developing a robust and effective obstacle detection and tracking system for Unmanned Surface Vehicle (USV) at marine environments is a challenging task. Research efforts have been made in this area during the past years by GRAAL lab at the university of Genova that resulted in a methodology for detecting and tracking obstacles on the image plane and, then, locating them in the 3D LiDAR point cloud. In this work, we continue on the developed system by, firstly, evaluating its performance on recently published marine datasets. Then, we integrate the different blocks of the system on ROS platform where we could test it in real-time on synchronized LiDAR and camera data collected in various marine conditions available in the MIT marine datasets. We present a thorough experimental analysis of the results obtained using two approaches; one that uses sensor fusion between the camera and LiDAR to detect and track the obstacles and the other uses only the LiDAR point cloud for the detection and tracking. In the end, we propose a hybrid approach that merges the advantages of both approaches to build an informative obstacles map of the surrounding environment to the USV.
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