Quantifying Uncertainty Towards Information-Centric Unmanned Navigation†

Highly imperfect, inconsistent information and incomplete a priori knowledge introduce uncertainty in sensor-centric 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 paper, within a probabilistic framework, we demonstrate the utility of estimationand informationtheoretic concepts towards quantifying uncertainty using entropy and mutual information metrics in various contexts of unmanned navigation via experimental results. Keywords— Bayes Theorem, Entropy, Information Evaluation, Sensor Uncertainty, Unmanned Navigation, LADAR.