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IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion

Summary

The best filter algorithm to fuse multiple sensor informations is the Kalman filter. To implement it for non-linear dynamic models (e.g. a car), analytic calculations for the matrices are necessary. In this talk, one can see, how the IPython Notebook and Sympy helps to develop an optimal filter to fuse sensor information from different sources (e.g. acceleration, speed and GPS position) to get an optimal estimate. more: http://balzer82.github.io/Kalman/

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