Sensor-centric navigation of unmanned ground vehicles (UGVs) operating in rugged and expansive terrains requires the competency to evaluate the utility of sensor information such that it results in intelligent behavior of the vehicles. Highly imperfect, inconsistent information and
incomplete a priori knowledge introduce uncertainty in such unmanned navigation systems. Understanding and quantifying uncertainty yields a measure of useful information that plays a critical role in several robotic navigation tasks such as sensor fusion, mapping, localization, path
planning and control. In this article, within a probabilistic framework, the utility of estimation and information-theoretic concepts towards quantifying uncertainty using entropy and mutual information metrics in various contexts of UGV navigation via experimental results is demonstrated.