用神经网络提升旋翼机在周期轨迹下的惯性定位精度
Quadrotor Neural Dead Reckoning in Periodic Trajectories
- 将惯性数据输入轻量网络，直接预测飞行器位置向量
- 室外误差降低27%，室内误差降低79%
- 仅需软件改动，适合无定位信号场景
在真实环境中，由于环境或硬件限制，四旋翼机常需在室内外纯惯性导航模式下运行。为缓解惯性漂移问题，已有研究提出结合四旋翼周期轨迹的端到端神经网络方法，通过回归飞行距离并融合基于惯性模型的航向估计来推断位置。本文提出一种面向周期轨迹的四旋翼神经死区推算方法，直接将惯性读数输入简洁高效的网络以估计位置向量。该方法在两台不同四旋翼上验证：一台室内运行，另一台室外运行。结果表明，相比其他深度学习方法，本方法在室外平均误差降低27%，室内平均误差降低79%，且仅需软件修改。更高的定位精度使四旋翼能无缝完成任务。
原文摘要 · Abstract (English)
In real world scenarios, due to environmental or hardware constraints, the quadrotor is forced to navigate in pure inertial navigation mode while operating indoors or outdoors. To mitigate inertial drift, end-to-end neural network approaches combined with quadrotor periodic trajectories were suggested. There, the quadrotor distance is regressed and combined with inertial model-based heading estimation, the quadrotor position vector is estimated. To further enhance positioning performance, in this paper we propose a quadrotor neural dead reckoning approach for quadrotors flying on periodic trajectories. In this case, the inertial readings are fed into a simple and efficient network to directly estimate the quadrotor position vector. Our approach was evaluated on two different quadrotors, one operating indoors while the other outdoors. Our approach improves the positioning accuracy of other deep-learning approaches, achieving an average 27% reduction in error outdoors and an average 79% reduction indoors, while requiring only software modifications. With the improved positioning accuracy achieved by our method, the quadrotor can seamlessly perform its tasks.
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