This paper presents an independent stereo-vision based positioning system for docking operations. The low-cost system consists of an object detector and different 3D reconstruction techniques. To address the challenge of robust detections in an unstructured and complex outdoor environment, a learning-based object detection model is proposed. The system employs a complementary modular approach that uses data-driven methods, utilizing data wherever required and traditional computer vision methods when the scope and complexity of the environment are reduced. Both, monocular and stereo-vision based methods are investigated for comparison. Furthermore, easily identifiable markers are utilized to obtain reference points, thus simplifying the localization task. A small unmanned surface vehicle (USV) with a LiDAR-based positioning system was exploited to verify that the proposed vision-based positioning system produces accurate measurements under various docking scenarios. Field experiments have proven that the developed system performs well and can supplement the traditional navigation system for safety-critical docking operations.

本文提出了一种用于对接操作的独立立体视觉定位系统。低成本系统由目标检测器和不同的 3D 重建技术组成。为了解决在非结构化和复杂的室外环境中进行鲁棒检测的挑战，提出了一种基于学习的对象检测模型。该系统采用互补的模块化方法，该方法使用数据驱动的方法，在需要时利用数据，并在环境的范围和复杂性降低时利用传统的计算机视觉方法。研究了基于单眼和立体视觉的方法以进行比较。此外，利用易于识别的标记来获取参考点，从而简化了定位任务。利用具有基于 LiDAR 的定位系统的小型无人水面车辆 (USV) 来验证所提出的基于视觉的定位系统在各种对接场景下都能产生准确的测量结果。现场实验证明，开发的系统性能良好，可以补充传统的导航系统，用于安全关键的对接操作。