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 animed aniflex complete 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 jointly compute the proposed variance estimators and the looney tunes knot on head 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.