High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper the use of standard visual odometry approaches and imply that the micro air vehicles will have to bridge considerable time intervals without position feedback. To this end, we propose an approach to trajectory estimation for drone racing that is computationally efficient and yet able to accurately estimate a micro air vehicle’s state (including biases) and parameters based on sparse, noisy observations of racing gates. The key concept of the approach is to optimize unknown and difficult-to-observe state variables so that the observations of the racing gates best fit with the known control inputs, estimated attitudes, and the quadrotor dynamics and aerodynamics during a time window. It is shown that a gradient-descent implementation of the proposed approach converges ∼4 times quicker to (approximately) correct bias values than a state-of-the-art 15-state extended Kalman filter. Moreover, it reaches a higher accuracy, as the predicted end-point of an open-loop turn is on average only ∼20 cm away from the actual end-point, while the extended Kalman filter and the gradient descent method with kinematic model only reach an accuracy of ∼50 cm. Although the approach is applied here to drone racing, it generalizes to other settings in which a micro air vehicle may only have sparse access to velocity and/or position measurements.

中文翻译：

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使用梯度下降进行高速飞行的飞行模型参数和状态估计

无GPS环境下的高速飞行是目前微型飞行器自主飞行研究的重要前沿。自主无人机比赛通过代表一个非常具有挑战性的案例来刺激这一领域的进步，包括急转弯、无纹理的地板和赛道周围的动态观众。这些特性阻碍了标准视觉里程计方法的使用，并意味着微型飞行器将不得不在没有位置反馈的情况下跨越相当长的时间间隔。为此，我们提出了一种用于无人机竞赛的轨迹估计方法，该方法在计算上是高效的，并且能够基于对赛车门的稀疏、嘈杂的观察来准确估计微型飞行器的状态（包括偏差）和参数。该方法的关键概念是优化未知和难以观察的状态变量，以便在时间窗口内对赛车门的观察最适合已知的控制输入、估计的姿态以及四旋翼动力学和空气动力学。结果表明，与最先进的 15 态扩展卡尔曼滤波器相比，所提出方法的梯度下降实现收敛到（大约）正确的偏差值快 4 倍。此外，它达到了更高的精度，因为开环转弯的预测终点距离实际终点平均只有约 20 cm，而扩展卡尔曼滤波器和带有运动学模型的梯度下降方法只能达到精度约为 50 厘米。虽然这种方法在这里应用于无人机比赛，