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

Blog Article

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 roman atwood gfuel estimators of multiple variances and are robust against outliers.An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators 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 koleston 55/44 based localization in an urban environment and data containing multipath problems demonstrates the application of the proposed technique.