Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a strategy for autonomous drone racing which is computationally more efficient than navigation methods like visual inertial odometry and simultaneous localization and mapping. This fast light-weight vision-based navigation algorithm estimates the position of the drone by fusing race gate detections with model dynamics predictions. Theoretical analysis and simulation results show the clear advantage compared to Kalman filtering when dealing with the relatively low frequency visual updates and occasional large outliers that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan", which was equipped with a Jevois smart-camera for a total of 72g. The test track consists of 3 laps around a 4-gate racing track. The gates spaced 4 meters apart and can be displaced from their supposed position. An average speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our knowledge, this flying platform is the smallest autonomous racing drone in the world and is 6 times lighter than the existing lightest autonomous racing drone setup (420g), while still being one of the fastest autonomous racing drones in the world.

中文翻译：

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用于 72 克无人机计算效率高的自主竞赛的视觉模型预测定位

无人机竞速正在成为风靡全球的电子竞技项目，击败最优秀的人类无人机竞速飞行员已迅速成为人工智能和机器人技术的新重大挑战。在本文中，我们提出了一种自主无人机竞赛的策略，该策略在计算上比视觉惯性里程计和同步定位和映射等导航方法更有效。这种基于视觉的快速轻量级导航算法通过将赛门检测与模型动力学预测相结合来估计无人机的位置。理论分析和仿真结果表明，与卡尔曼滤波相比，在处理快速无人机比赛中出现的相对低频的视觉更新和偶尔出现的大的异常值时，具有明显的优势。飞行测试是在名为“垃圾桶”的小型赛车四旋翼飞行器上进行的，其中配备了 Jevois 智能相机，总重量为 72 克。测试赛道由围绕 4 门赛道的 3 圈组成。门相距 4 米，可以从它们假定的位置移动。平均速度为 2m/s，最大速度为 2.6m/s。据我们所知，这个飞行平台是世界上最小的自动赛车无人机，比现有最轻的自动赛车无人机装置（420g）轻 6 倍，同时仍然是世界上最快的自动赛车无人机之一。