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Information Fusion in Navigation Systems via Factor Graph Based Incremental Smoothing

V. Indelman, S. Williams, Michael Kaess and F. Dellaert
Journal Article, Journal of Robotics and Autonomous Systems, RAS, Vol. 61, No. 8, pp. 721-738, August, 2013

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This paper presents a new approach for high-rate information fusion in modern inertial navigation systems, that have a variety of sensors operating at dif- ferent frequencies. Optimal information fusion corresponds to calculating the maximum a posteriori estimate over the joint probability distribution function (pdf) of all states, a computationally-expensive process in the general case. Our approach consists of two key components, which yields a exible, high-rate, near-optimal inertial navigation system. First, the joint pdf is represented us- ing a graphical model, the factor graph, that fully exploits the system sparsity and provides a plug and play capability that easily accommodates the addition and removal of measurement sources. Second, an e cient incremental infer- ence algorithm over the factor graph is applied, whose performance approaches the solution that would be obtained by a computationally-expensive batch op- timization at a fraction of the computational cost. To further aid high-rate performance, we introduce an equivalent IMU factor based on a recently de- veloped technique for IMU pre-integration, drastically reducing the number of states that must be added to the system. The proposed approach is experimen- tally validated using real IMU and imagery data that was recorded by a ground vehicle, and a statistical performance study is conducted in a simulated aerial scenario. A comparison to conventional xed-lag smoothing demonstrates that our method provides a considerably improved trade-o between computational complexity and performance.

author = {V. Indelman and S. Williams and Michael Kaess and F. Dellaert},
title = {Information Fusion in Navigation Systems via Factor Graph Based Incremental Smoothing},
journal = {Journal of Robotics and Autonomous Systems, RAS},
year = {2013},
month = {August},
volume = {61},
number = {8},
pages = {721-738},
keywords = {inertial navigation, multi-sensor fusion, graphical models, incremental inference, plug and play architecture},
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