Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the thrust measurements. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual-inertial odometry as well as the state-of-the-art learned-inertial odometry in estimating the pose of an autonomous racing drone. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight.

翻译：惯性里程计是解决敏捷四旋翼飞行状态估计问题的一种具有吸引力的方案。该方案成本低廉、重量轻巧，且不受感知退化影响。然而，仅依赖惯性测量积分进行状态估计并不可行。此类测量中存在的误差和时变偏差会导致位姿估计中累积大量漂移。近年来，惯性里程计在行人运动估计领域取得了显著进展，现有最优算法依赖于学习人类特有的运动先验，但这类先验无法迁移至无人机场景。本文针对自主无人机竞速任务，提出了一种仅以惯性测量单元（IMU）为唯一传感器模态的基于学习的里程计算法。系统的核心思想是将由惯性测量驱动的模型化滤波器与可获取推力测量的学习模块相结合。实验表明，在自主竞速无人机的位姿估计中，本文提出的惯性里程计算法优于现有最优的基于滤波和优化的视觉惯性里程计，以及最优的基于学习的惯性里程计。此外，本系统的性能与使用摄像头并利用已知门框位置及外观的视觉惯性里程计方案相当。我们相信，该技术在自主无人机竞速中的应用将为敏捷四旋翼飞行的惯性里程计研究开辟新方向。