Title:
Factor Graph Based Incremental Smoothing in Inertial Navigation Systems
Factor Graph Based Incremental Smoothing in Inertial Navigation Systems
Author(s)
Indelman, Vadim
Williams, Stephen
Kaess, Michael
Dellaert, Frank
Williams, Stephen
Kaess, Michael
Dellaert, Frank
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Abstract
This paper describes a new approach for information
fusion in inertial navigation systems. In contrast to the
commonly used filtering techniques, the proposed approach is
based on a non-linear optimization for processing incoming
measurements from the inertial measurement unit (IMU) and
any other available sensors into a navigation solution. A factor
graph formulation is introduced that allows multi-rate, asynchronous,
and possibly delayed measurements to be incorporated
in a natural way. This method, based on a recently developed
incremental smoother, automatically determines the number of
states to recompute at each step, effectively acting as an adaptive
fixed-lag smoother. This yields an efficient and general framework
for information fusion, providing nearly-optimal state estimates.
In particular, incoming IMU measurements can be processed in
real time regardless to the size of the graph. The proposed method
is demonstrated in a simulated environment using IMU, GPS
and stereo vision measurements and compared to the optimal
solution obtained by a full non-linear batch optimization and to
a conventional extended Kalman filter (EKF).
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Date Issued
2012-07
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Text
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Proceedings