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Multipath-Based SLAM Exploiting AoA and Amplitude Information

In this paper, we present a Bayesian feature-based simultaneous localization and mapping (SLAM) algorithm that exploits multipath components (MPCs) in radio-signals. The proposed belief propagation (BP)-based algorithm enables the estimation of the position, velocity, and orientation of the mobile agent equipped with an antenna array by utilizing the delays and the angle-of-arrivals (AoAs) of the MPCs. The proposed algorithm also exploits the statistics of the complex amplitudes of MPC parameters, i.e. amplitude information (AI), to calculate the detection probabilities of the features. It is therefore suitable for unknown and time-varying detection probabilities. For improved initialization of new virtual anchor (VA) positions, the states of unobserved potential VAs are modeled as a random finite set and propagated in time by means of a "zero-measurement" probability hypothesis density filter. We analyze the proposed BP-AI-based SLAM algorithm using synthetic and real measurements enabling robust localization in a challenging environment.