State estimation of an autonomous helicopter using Kalman filtering

Myungsoo Jun, Stergios I. Roumeliotis, Gaurav S. Sukhatme

Research output: Chapter in Book/Report/Conference proceedingConference contribution

66 Scopus citations

Abstract

This paper presents a technique to accurately estimate the state of a robot helicopter using a combination of gyroscopes, accelerometers, inclinometers and GPS. Simulation results of state estimation of the helicopter are presented using Kalman filtering based on sensor modeling. The number of estimated states of helicopter is nine: three attitudes (θ, φ, ψ) from the gyroscopes, three accelerations (qq) and three positions (x, y, z) from the accelerometers. Two Kalman filters were used, one for the gyroscope data and the other for the accelerometer data. Our approach is unique because it explicitly avoids dynamic modeling of the system and allows for an elegant combination of sensor data available at different frequencies. We also describe the larger context in which this work is embedded, namely the design and implementation of an autonomous robot helicopter.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1346-1353
Number of pages8
ISBN (Print)0780351843
StatePublished - Jan 1 1999
Event1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients' - Kyongju, S Korea
Duration: Oct 17 1999Oct 21 1999

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume3

Other

Other1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'99): Human and Environment Friendly Robots whith High Intelligence and Emotional Quotients'
CityKyongju, S Korea
Period10/17/9910/21/99

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