  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 Radio Frequency Identification (RFID)
tags, also referred to as landmarks, as range sensors that are carried by the
vehicle and are deployed in the environment 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
required for localization before the start of the mission, given some
information about the environment. The main constraint of the problem is to
keep the maximum uncertainty in the position estimate near a desired value. In
this article, we combine techniques from estimation, machine learning, and
mixed-integer convex optimization to develop a systematic method to perform
localization and navigate the UV through the environment while ensuring minimum
number of landmarks are used and all the mission constraints are satisfied.
