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 aboriginal flag beanie 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 rab bck-s4 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.