We present an approach to autonomous drone racing inspired by how a human pilot learns a race track. Human pilots drive around the track multiple times to familiarise themselves with the track and find key points that allow them to complete the track without the risk of collision. This paper proposes a three-stage approach: exploration, navigation, and refinement. Our approach does not require prior knowledge about the race track, such as the number of gates, their positions, and their orientations. Instead, we use a trained neural pilot called DeepPilot to return basic flight commands from camera images where a gate is visible to navigate an unknown race track and a Single Shot Detector to visually detect the gates during the exploration stage to identify points of interest. These points are then used in the navigation stage as waypoints in a flight controller to enable faster flight and navigate the entire race track. Finally, in the refinement stage, we use the methodology developed in stages 1 and 2, to generate novel data to re-train DeepPilot, which produces more realistic manoeuvres for when the drone has to cross a gate. In this sense, similar to the original work, rather than generating examples by flying in a full track, we use small tracks of three gates to discover effective waypoints to be followed by the waypoint controller. This produces novel training data for DeepPilot without human intervention. By training with this new data, DeepPilot significantly improves its performance by increasing its flight speed twice w.r.t. its original version. Also, for this stage 3, we required 66% less training data than in the original DeepPilot without compromising the effectiveness of DeepPilot to enable a drone to autonomously fly in a racetrack.

中文翻译：

---

有效的培训来提高 DeepPilot

我们提出了一种自主无人机竞赛的方法，其灵感来自于人类飞行员如何学习赛道。人类飞行员在赛道上行驶多次，以熟悉赛道并找到关键点，使他们能够在没有碰撞风险的情况下完成赛道。本文提出了一个三阶段的方法：探索、导航和细化。我们的方法不需要有关赛道的先验知识，例如大门的数量、它们的位置和方向。相反，我们使用经过训练的神经飞行员 DeepPilot 从相机图像返回基本飞行命令，其中可见门以导航未知赛道，并使用单次探测器在探索阶段视觉检测门以识别兴趣点。然后，这些点在导航阶段用作飞行控制器中的航路点，以实现更快的飞行并导航整个赛道。最后，在细化阶段，我们使用阶段 1 和阶段 2 中开发的方法来生成新数据来重新训练 DeepPilot，从而在无人机必须穿过大门时产生更真实的机动。从这个意义上说，与原始工作类似，我们不是通过在完整轨道上飞行来生成示例，而是使用三个门的小轨道来发现航路点控制器遵循的有效航路点。这会在无需人工干预的情况下为 DeepPilot 生成新颖的训练数据。通过使用这些新数据进行训练，DeepPilot 的飞行速度比原始版本提高了一倍，从而显着提高了性能。此外，在第 3 阶段，我们需要比原始 DeepPilot 少 66% 的训练数据，而不会影响 DeepPilot 使无人机能够在赛道上自主飞行的有效性。