IDF-MFL: Infrastructure-free and Drift-free Magnetic Field Localization for Mobile Robot

TL;DR

This paper introduces IDF-MFL, a novel magnetic field-based localization system for mobile robots that operates without pre-installed infrastructure, using ambient magnetic field data and robust optimization to achieve high accuracy in real-world environments.

Contribution

The paper presents a new infrastructure-free, drift-free magnetic field localization method with a robust optimization algorithm for dynamic environments.

Findings

Achieves high-accuracy localization in real-world scenarios

Operates reliably without pre-installed infrastructure

Demonstrates real-time performance in diverse environments

Abstract

In recent years, infrastructure-based localization methods have achieved significant progress thanks to their reliable and drift-free localization capability. However, the pre-installed infrastructures suffer from inflexibilities and high maintenance costs. This poses an interesting problem of how to develop a drift-free localization system without using the pre-installed infrastructures. In this paper, an infrastructure-free and drift-free localization system is proposed using the ambient magnetic field (MF) information, namely IDF-MFL. IDF-MFL is infrastructure-free thanks to the high distinctiveness of the ambient MF information produced by inherent ferromagnetic objects in the environment, such as steel and reinforced concrete structures of buildings, and underground pipelines. The MF-based localization problem is defined as a stochastic optimization problem with the consideration of the non-Gaussian heavy-tailed noise introduced by MF measurement outliers (caused by dynamic ferromagnetic objects), and an outlier-robust state estimation algorithm is derived to find the optimal distribution of robot state that makes the expectation of MF matching cost achieves its lower bound. The proposed method is evaluated in multiple scenarios, including experiments on high-fidelity simulation, and real-world environments. The results demonstrate that the proposed method can achieve high-accuracy, reliable, and real-time localization without any pre-installed infrastructures.