Data Fusion for Radio Frequency SLAM with Robust Sampling

TL;DR

This paper improves radio frequency SLAM for indoor localization by introducing a robust sampling technique that enhances the accuracy and convergence of MVA-based methods in challenging environments.

Contribution

The paper proposes an improved sampling method for MVA-based RF-SLAM, addressing convergence issues in difficult scenarios and demonstrating enhanced performance.

Findings

Significant performance improvements in challenging scenarios

Robust sampling technique outperforms conventional bootstrap sampling

Enhanced accuracy in localizing flat surfaces and agents

Abstract

Precise indoor localization remains a challenging problem for a variety of essential applications. A promising approach to address this problem is to exchange radio signals between mobile agents and static physical anchors (PAs) that bounce off flat surfaces in the indoor environment. Radio frequency simultaneous localization and mapping (RF-SLAM) methods can be used to jointly estimates the time-varying location of agents as well as the static locations of the flat surfaces. Recent work on RF-SLAM methods has shown that each surface can be efficiently represented by a single master virtual anchor (MVA). The measurement model related to this MVA-based RF-SLAM method is highly nonlinear. Thus, Bayesian estimation relies on sampling-based techniques. The original MVA-based RF-SLAM method employs conventional "bootstrap" sampling. In challenging scenarios it was observed that the original method might converge to incorrect MVA positions corresponding to local maxima. In this paper, we introduce MVA-based RF-SLAM with an improved sampling technique that succeeds in the aforementioned challenging scenarios. Our simulation results demonstrate significant performance advantages.