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

O. Arda Vanli,Clark N. Taylor

doi:10.1186/s13634-022-00837-8

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 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 based localization in an urban environment and data containing multipath problems demonstrates the application of the proposed technique.

Highlights

An important challenge in Global Positioning System (GPS)-based localization is the non-line-of-sight or multipath problem, commonly encountered in urban environments
Some robust estimation approaches in the recent literature to remedy this issue are based on data weighting: M-estimators that rely on downweighting of outlying observations [2], mixture distributions that explicitly model the outlying observations in the sensor model [3], switchable constraints that utilize switch variables to down-weight individual pose constraints [1, 4], dynamic covariance scaling [5] and Receiver Autonomous Integrity Monitoring (RAIM) that monitors the integrity of satellites [6]
6.1 Mobile robot experiments Using the example mobile robot systems, we evaluate the performance of the factor graph noise variance and localization estimates. 1000 Monte Carlo replications of the robot simulation were generated for n = 20 timesteps and in each replication the location sequence of the robot and the noise variances are estimated

Summary

Introduction

An important challenge in Global Positioning System (GPS)-based localization is the non-line-of-sight or multipath problem, commonly encountered in urban environments. Some robust estimation approaches in the recent literature to remedy this issue are based on data weighting: M-estimators that rely on downweighting of outlying observations [2], mixture distributions that explicitly model the outlying observations in the sensor model [3], switchable constraints that utilize switch variables to down-weight individual pose constraints [1, 4], dynamic covariance scaling [5] and Receiver Autonomous Integrity Monitoring (RAIM) that monitors the integrity of satellites [6] None of these robust estimation approaches considered the unbiasedness of the variance estimators.

Methods

Results

Conclusion