Simultaneous localization and mapping, Equations, Mathematical model, Aircraft, Accelerometers, Estimation, Aircraft navigation


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.

Original Publication Citation

Leishman, R., Macdonald, J., Quebe, S., Ferrin, J., Beard, R., and McLain, T. Utilizing an Improved Rotorcraft Dynamic Model in State Estimation, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5173-5178, September 2011, San Francisco, California.

Document Type

Conference Paper

Publication Date


Permanent URL






Ira A. Fulton College of Engineering and Technology


Mechanical Engineering

University Standing at Time of Publication

Full Professor