Robust Indoor Localization with Ranging-IMU Fusion

TL;DR

This paper presents a robust indoor localization method that fuses wireless ranging data with inertial measurements, effectively handling multipath disturbances to achieve high accuracy in challenging environments.

Contribution

It introduces a novel asymmetric noise model for multipath disturbances and a Levenberg-Marquardt-based fusion algorithm tailored for range-IMU data integration.

Findings

Achieves approximately 0.3m localization accuracy in real office environments.

Maintains high accuracy with range measurements as infrequent as 1Hz.

Demonstrates robustness against multipath propagation challenges.

Abstract

Indoor wireless ranging localization is a promising approach for low-power and high-accuracy localization of wearable devices. A primary challenge in this domain stems from non-line of sight propagation of radio waves. This study tackles a fundamental issue in wireless ranging: the unpredictability of real-time multipath determination, especially in challenging conditions such as when there is no direct line of sight. We achieve this by fusing range measurements with inertial measurements obtained from a low cost Inertial Measurement Unit (IMU). For this purpose, we introduce a novel asymmetric noise model crafted specifically for non-Gaussian multipath disturbances. Additionally, we present a novel Levenberg-Marquardt (LM)-family trust-region adaptation of the iSAM2 fusion algorithm, which is optimized for robust performance for our ranging-IMU fusion problem. We evaluate our solution in a densely occupied real office environment. Our proposed solution can achieve temporally consistent localization with an average absolute accuracy of $\sim$0.3m in real-world settings. Furthermore, our results indicate that we can achieve comparable accuracy even with infrequent (1Hz) range measurements.