Roumeliotis
S. I., Sukhatme G.S., and Bekey G.A. :
"Smoother based 3-D Attitude Estimation for Mobile Robot Localization"
Abstract:
The mobile robot localization
problem is decomposed into two stages; attitude estimation followed by position
estimation. The innovation of our method is the use of a smoother, in the attitude
estimation loop that outperforms other Kalman fiter based techniques in estimate
accuracy. The smoother exploits the special nature of the data fused; high frequency
inertial sensor (gyroscope) data and low frequency absolute orientation data (from
a compass or sun sensor). Two Kalman filters form the smoother. During each time
interval one of them propagates the attitude estimate forward in time until it
is updated by an absolute orientation sensor. At this time, the second filter
propagates the recently renewed estimate back in time. The smoother optimally
exploits the limited observability of the system by combining the outcome of the
two filters. The system model uses gyro modeling which relies on integrating the
kinematic equations to propagate the attitude estimates and obviates the need
for complex dynamic modeling. The Indirect (error state) form of the Kalman filter
is developed for both parts of the smoother. The proposed approach is independent
of the robot structure and the morphology of the ground. It can easily be transfered
to another robot which has an equivalent set of sensors. Quaternions are used
for the 3D attitude representation, mainly for practical reasons discussed in
the paper. The proposed innovative algorithm is tested in simulation and the overall
improvement in position estimation is demonstrated.
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