Utilizing an improved rotorcraft dynamic model in state estimation

Abstract

Multirotor aircraft have become a popular platform for indoor flight. To navigate these vehicles indoors through an unknown environment requires the use of a SLAM algorithm, which can be processing intensive. However, their size, weight, and power capacity limit the processing capabilities available onboard. In this paper, we describe an approach to state estimation that helps to alleviate this problem. By using an improved dynamic model we show how to more accurately estimate the aircraft states than can be done with the traditional approach of integrating IMU measurements. The estimation is done with relatively infrequent corrections from accelerometers (40Hz) and even less frequent updates from a vision-based SLAM algorithm (2-5 Hz). This benefit of requiring less frequent updates from processing intensive sources comes without significant increase in the estimator's complexity.

Relative navigation and control of a hexacopter

“…This improved model of the vehicle dynamics provides the ability to fully utilize the information contained in the IMU measurements. As a consequence, estimation accuracy improves and the requirements for view matching or any other exteroceptive measurement updates are reduced [12], [13].…”

“…Due to the fast dynamics of the hexacopter platform, we require state estimates at a quick rate for the control to stabilize the platform. In contrast to our previous work [13], we are now using a single filter for all states.…”