Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning
Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning
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DOI：
10.1109/lra.2018.2808368
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发表时间：
2018-07-01
影响因子：
5.2
通讯作者：
Shim, David Hyunchul
中科院分区：
文献类型：
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作者：
Jung, Sunggoo;Hwang, Sunyou;Shim, David Hyunchul
在自动无人机比赛中，无人机被要求快速飞过大门而不发生任何碰撞。因此，利用计算机视觉对闸门进行可靠的检测是非常重要的。然而，由于光照条件的变化和看到的门重叠等复杂情况，基于门的颜色和几何的传统图像处理算法在实际比赛中往往会失败。在这封信中，我们引入了卷积神经网络来稳健地估计门的中心。利用探测结果，我们应用了视线制导算法。所提出的算法使用低成本、现成的硬件进行验证。所有视觉处理都在NVIDIA Jetson TX2嵌入式计算机上实时执行。在大量的测试中，我们提出的框架成功地在室内环境中表现出快速可靠的检测和导航性能。
In autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image processing algorithms based on color and geometry of the gates tend to fail during the actual racing. In this letter, we introduce a convolutional neural network to estimate the center of a gate robustly. Using the detection results, we apply a line-of-sight guidance algorithm. The proposed algorithm is implemented using low cost, off-the-shelf hardware for validation. All vision processing is performed in real time on the onboard NVIDIA Jetson TX2 embedded computer. In a number of tests our proposed framework successfully exhibited fast and reliable detection and navigation performance in indoor environment.