Factor Graph Based Incremental Smoothing in Inertial Navigation Systems

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“Factor Graph Based Incremental Smoothing in Inertial Navigation Systems” by V. Indelman, S. Williams, M. Kaess, and F. Dellaert. In Intl. Conf. on Information Fusion, FUSION, (Singapore), July 2012, pp. 2154-2161.

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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BibTeX entry:

@inproceedings{Indelman12fusion,
   author = {V. Indelman and S. Williams and M. Kaess and F. Dellaert},
   title = {Factor Graph Based Incremental Smoothing in Inertial
	Navigation Systems},
   booktitle = {Intl. Conf. on Information Fusion, FUSION},
   pages = {2154-2161},
   address = {Singapore},
   month = {Jul},
   year = {2012}
}
Last updated: June 21, 2014