Quadrotor Neural Dead Reckoning in Periodic Trajectories

TL;DR

This paper introduces a neural dead reckoning method for quadrotors on periodic trajectories, significantly improving indoor and outdoor positioning accuracy with minimal software changes.

Contribution

It proposes a simple neural network approach for direct position estimation from inertial data, enhancing accuracy over previous deep-learning methods.

Findings

27% error reduction outdoors

79% error reduction indoors

Applicable to different quadrotor platforms

Abstract

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.