We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. It can detect complete, partial, and imperfect ellipses in extreme weather and lighting conditions and is lightweight enough to be used on robots' resource-limited onboard computers. The method is used on an example application of autonomous UAV landing on a fast-moving vehicle to show its performance indoors, outdoors, and in simulation on a real-world robotics task. The comparison with other well-known ellipse detection methods shows that our proposed algorithm outperforms other methods with the F1 score of 0.981 on a dataset with over 1500 frames. The videos of experiments, the source codes, and the collected dataset are provided with the paper.
翻译:我们提出一种新的算法,用于实时探测和跟踪适合现实世界机器人应用的椭圆形模式。 这种方法适合图像框中每个轮廓的省略法, 拒绝没有产生良好效果的省略法。 它可以在极端天气和照明条件下探测完整、 局部和不完善的省略法, 并且足够轻到可用于机器人在机上限制资源计算机上使用。 这种方法用于自动自动的UAV降落在快速移动的飞行器上, 以展示其室内、 室外和模拟真实世界机器人任务的性能。 与其他众所周知的椭圆探测方法的比较表明, 我们提议的算法优于1500多框架数据集上的0. 981的F1分的其他方法。 实验录像、 源代码和收集的数据集用纸张提供。