Abstract
In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.
Highlights
These two process noise variances are expressed as σp21 and σp22, where σp21 can be chosen as a rule of thumb and σp22 is at least one order of magnitude larger than σp21 to describe the state mutation of the user equipment (UE)
The proposed algorithm adds the evolutions of the process noise variance and the clutter intensity to the Bayesian model compared with the conventional belief propagation (BP)-Simultaneous localization and mapping (SLAM) algorithm; its computational complexity is proportional to O( Npar + Nb2 + Ng2 ) [33], where Nb and Ng denote the number of process noise variances and clutter intensity, respectively
In multipath-assisted SLAM using radio signals, the estimation of the process noise and the clutter intensity is difficult in practice
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In multipath-assisted SLAM, we have to address the uncertainty of two important model parameters, process noise and clutter intensity, in addition to the detection probability. Most of the existing multipath-assisted SLAM algorithms regard the process noise and clutter intensity as known and fixed. We integrate the augmented state of the UE and the new variable node into a factor graph representing the Bayesian model of BP-SLAM and use the BP message passing algorithm to calculate marginal posterior distributions of the process noise, the clutter intensity, the UE and other features to realize the online adjustment of the process noise and the clutter intensity in multipath-assisted. The simulation results demonstrate that the proposed algorithm can adaptively learn the process noise and clutter intensity for SLAM, improving the robustness of the multipath-assisted SLAM system.
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