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Iterative smoothing approach using Gaussian mixture models for nonlinear estimation

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2 Author(s)
Lee, D.J. ; Sibley Sch. of Mech. & Aerosp. Eng., Cornell Univ., Ithaca, NY, USA ; Campbell, M.E.

An iterative smoothing algorithm is developed using Gaussian mixture models in order to tackle challenging nonlinear estimation problems. Gaussian mixture models naturally capture nonlinear and non-Gaussian systems, while smoothing algorithms provide ability to update using measurements obtained in the past. A tree structure and Gaussian distribution splitting method are proposed to mitigate nonlinearity effects and complexities. Two methods, Children Collapsing and Parent Splitting, are developed to utilize sigma-points smoother for Gaussian mixture model. An indoor localization problem is used to explore and validate the approach. Performance of these new methods is compared to a baseline sigma-points smoother, in both simulation and experiment, and shows much improvement in overall error compared to the truth.

Published in:

Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on

Date of Conference:

7-12 Oct. 2012