  Simultaneous localization and mapping (SLAM) during communication is
emerging. This technology promises to provide information on propagation
environments and transceivers' location, thus creating several new services and
applications for the Internet of Things and environment-aware communication.
Using crowdsourcing data collected by multiple agents appears to be much
potential for enhancing SLAM performance. However, the measurement
uncertainties in practice and biased estimations from multiple agents may
result in serious errors. This study develops a robust SLAM method with
measurement plug-and-play and crowdsourcing mechanisms to address the above
problems. First, we divide measurements into different categories according to
their unknown biases and realize a measurement plug-and-play mechanism by
extending the classic belief propagation (BP)-based SLAM method. The proposed
mechanism can obtain the time-varying agent location, radio features, and
corresponding measurement biases (such as clock bias, orientation bias, and
received signal strength model parameters), with high accuracy and robustness
in challenging scenarios without any prior information on anchors and agents.
Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which
multiple agents cooperate to construct and refine the radio map in a
decentralized manner. Our study presents the first BP-based crowdsourcing that
resolves the "double count" and "data reliability" problems through the
flexible application of probabilistic data association methods. Numerical
results reveal that the crowdsourcing mechanism can further improve the
accuracy of the mapping result, which, in turn, ensures the decimeter-level
localization accuracy of each agent in a challenging propagation environment.
