AI-enhanced Direct SLAM: A Principled Approach to Unsupervised Learning in Bayesian Inference

Abstract

In this paper, we propose an artificial intelligence (AI)-enhanced hybrid simultaneous localization and mapping (SLAM) method that performs Bayesian inference directly on raw radio-frequency (RF) signals while learning an environment model in an unsupervised manner. The approach combines a physically interpretable signal model for line-of-sight (LOS) components with an AI model that captures multipath component statistics. Building on this formulation, we develop a particle-based sumproduct algorithm (SPA) on a factor graph that jointly estimates the mobile terminal (MT) state, visibility, multipath parameters, and noise variances, and integrate it into a variational framework that maximizes the evidence lower bound (ELBO) to learn the neural network (NN) parametrization directly from measurements. We further present a highly efficient GPU-based implementation that enables parallel likelihood evaluation across particles and base stations (BSs). Simulation results in multipath environments demonstrate that the proposed method learns the generative, environment-dependent signal model in an unsupervised manner while accurately localizing the MT and effectively exploiting the learned map in obstructed-line-of-sight (OLOS) scenarios.