Robust error estimation based on factor-graph models for non-line-of-sight localization

Robust error estimation based on factor-graph models for non-line-of-sight localization

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Abstract This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions.A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers.An iteratively re-weighted least squares algorithm is proposed to guerlain ideal cologne jointly compute the proposed variance estimators milwaukee 2485-22 and the state estimates for the nonlinear factor graph optimization.

The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement outlier scenarios.A case study involving a Global Positioning System based localization in an urban environment and data containing multipath problems demonstrates the application of the proposed technique.