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.
翻译:在通信过程中,正在出现同时的本地化和绘图(SLAM),这一技术有望提供关于传播环境和收发机位置的信息,从而为Things互联网和环境意识通信创建若干新的服务和应用程序。利用由多个代理机构收集的众包数据似乎大有可能提高SLAM的性能。然而,在实践中的测量不确定性和来自多个代理机构的偏差估计可能导致严重错误。这一研究开发了一个强有力的SLAM方法,其中含有处理上述问题的衡量插件和众包机制。首先,我们根据不同的类别,根据其未知的偏差,将测量分为不同的类别,并通过扩展传统的基于Thims的SLAM方法,实现衡量插件和游戏机制。拟议机制可以获取时间变化的代理机构地点、无线电功能和相应的计量偏差(如时偏差、方向偏差和从多个代理机构获得信号强度模型参数),从而在没有事先关于锚点和代理机构的任何信息的情况下,在具有高度准确性和稳妥性的情景中,我们建立了一个基于SLAMM机制,其中多个代理机构开展合作,以便进一步构建和完善基于SB级的准确度的准确度,从而通过“稳定化的准确度评估”的每个数据记录,我们的研究,可以提出“以稳定度的准确度的准确度,从而改进了“对数字的准确度的准确度的准确度”的图像的每一个度。