  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.
