IEEE Robotics & Automation Magazine - December 2013 - 32
Improved
State Estimation
in Quadrotor MAVs
A Novel Drift-Free Velocity Estimator
By Dinuka Abeywardena, Sarath Kodagoda,
Gamini Dissanayake, and Rohan Munasinghe
Digital Object Identifier 10.1109/MRA.2012.2225472
Date of publication: 10 June 2013
32
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IEEE ROBOTICS & AUTOMATION MAGAZINE
*
DECEMBER 2013
IMAGE LICENSED BY INGRAM PUBLISHING
Q
uadrotor microaerial vehicles
(MAVs) are simple robotic platforms with regard to their construction. In their basic form, they
are no more than two counterrotating propeller pairs attached symmetrically to a
rigid crosslike frame, along with the means to
control the speed of each individual propeller.
This symmetric design has enabled the quadrotor
to become a simple but powerful vertical takeoff
and landing aerial platform popular among the
robotics community.
With this simplicity comes the burden of controlling motion in three-dimensional (3-D) space
using just four actuators. The underactuated and
coupled dynamics of the quadrotor make it
nearly impossible for a human pilot to gain control over it, unless a well-tuned control system is used. Such a
control system is also vital if autonomy is a goal, as is the case
with most MAVs. Estimates of controlled states and their
derivatives are essential for any control system, and where
those estimates are accurate and frequent in time, it has been
demonstrated that quadrotors have extreme maneuverability
and agility [1].
With regard to design, however, the MAVs are limited in
their payload capacity and, with this limitation, obtaining
accurate and fast state estimates becomes a challenge. For
example, microelectromechanical systems (MEMS) inertial
sensors can provide fast but coarse state estimates [2],
whereas exteroceptive sensors, such as lasers and cameras [3],
render more accurate state estimates, albeit at a slower rate.
Attempts to merge these two sensing domains are frequent in
the MAV literature [4], [5], and an application of similar ideas
to quadrotors was presented in [6].
One aspect common to most MAV state estimators is
their use of inertial sensors. Typically, gyroscopes, accelerometers, and magnetometers are used for the purpose of attitude estimation [7]. Based on a long history of research in
inertial navigation systems, the sensor fusion algorithms
usually employed for this task make use of the equations of
motion of the sensing unit in 3-D space. The main advantage
of this approach is that these generic estimators are specific
only to the sensor package geometry and can be used independently of the platform on which the sensors are mounted.
1070-9932/13/$31.00©2013IEEE
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