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In this paper, we study the three-dimensional (3D) simultaneous localizationand mapping (SLAM) problem in complex outdoor and indoor environments basedonly on millimeter-wave (mmWave) wireless communication signals. Firstly, wepropose a deep-learning based mapping (DLM) algorithm that can leverage thereflections point on the first-order none line-of-sight (NLOS) communicationslinks (CLs) to build the 3D point cloud map of the environment. Specifically,we design a classification neural network to identify the first-order NLOS CLand theoretically calculate the geometric coordinates of the reflection pointson it. Secondly, we take the advantage of both the inertial measurement unitand the beam-squint assisted localization method to realize real-time andprecise localizations. Then, combining the DLM and the adopted localizationalgorithm, we develop the communication-based SLAM (C-SLAM) framework that cancarry out SLAM without any prior knowledge of the environment. Moreover,extensive simulations of both complex outdoor and indoor environments validatethe effectiveness of our approach.

Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping