A Belief Propagation Approach for Direct Multipath-Based SLAM

TL;DR

This paper introduces a novel belief propagation-based SLAM method that directly utilizes raw radio signals without preprocessing, improving accuracy over traditional channel-estimator-based approaches.

Contribution

It presents a new statistical model and a factor graph framework enabling direct multipath-based SLAM from received radio signals, bypassing feature extraction stages.

Findings

Outperforms existing methods relying on channel estimation

Demonstrates effectiveness in realistic SISO scenarios

Achieves higher localization accuracy

Abstract

In this work, we develop a multipath-based simultaneous localization and mapping (SLAM) method that can directly be applied to received radio signals. In existing multipath-based SLAM approaches, a channel estimator is used as a preprocessing stage that reduces data flow and computational complexity by extracting features related to multipath components (MPCs). We aim to avoid any preprocessing stage that may lead to a loss of relevant information. The presented method relies on a new statistical model for the data generation process of the received radio signal that can be represented by a factor graph. This factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that directly uses received radio signals as measurements. Simulation results in a realistic scenario with a single-input single-output (SISO) channel demonstrate that the proposed direct method for radio-based SLAM outperforms state-of-the-art methods that rely on a channel estimator.