This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions.
Robust non-linear smoothing for vehicle state estimation
01.06.2013
492391 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
ROBUST NON-LINEAR SMOOTHING FOR VEHICLE STATE ESTIMATION
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