This paper presents a novel data-driven navigation system to navigate an Unmanned Vehicle (UV) in GPS-denied, feature-deficient environments such as tunnels, or mines. The method utilizes landmarks that vehicle can deploy and measure range from to enable localization as the vehicle traverses its pre-defined path through the tunnel. A key question that arises in such scenario is to estimate and reduce the number of landmarks that needs to be deployed for localization before the start of the mission, given some information about the environment. The main focus is to keep the maximum position uncertainty at a desired value. In this article, we develop a novel vehicle navigation system in GPS-denied, feature-deficient environment by combining techniques from estimation, machine learning, and mixed-integer convex optimization. This article develops a novel, systematic method to perform localization and navigate the UV through the environment with minimum number of landmarks while maintaining desired localization accuracy. We also present extensive simulation experiments on different scenarios that corroborate the effectiveness of the proposed navigation system.

翻译：本文展示了一个新的由数据驱动的导航系统,用于在诸如隧道或地雷等GPS封闭、地貌不全的环境中导航无人驾驶车辆(UV),该方法使用车辆能够部署和测量的地标,这些地标从车辆穿越其预设的通道穿过隧道,就能够实现本地化。在这种情景中出现的一个关键问题是,根据有关环境的一些信息,估计和减少在任务开始之前需要部署的地标数量。主要重点是将最大位置的不确定性保持在理想值。在本篇文章中,我们开发了一种由GPS封闭、地貌不全的环境的新型车辆导航系统,将估算、机器学习和混合英特格内韦克斯优化等技术结合起来。这篇文章开发了一种新的系统方法,以最小的地标数量在环境中进行本地化和导航,同时保持理想的本地化准确性。我们还对证实拟议导航系统有效性的不同情景进行了广泛的模拟实验。