Quantifying Uncertainty Towards Information-Centric Unmanned Navigation
Rajmohan (. Madhavan, Elena R. Messina
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 estimation- and informationtheoretic concepts towards quantifying uncertainty using entropy and mutual information metrics in various contexts of unmanned navigation via experimental results.
Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop
August 16-18, 2003
Gaithersburg, MD, USA
Performance Metrics for Intelligent Systems (PerMIS) Workshop
Bayes Theorem, Entropy, Information Evaluation, LADAR., Sensor Uncertainty, Unmanned Navigation
and Messina, E.
Quantifying Uncertainty Towards Information-Centric Unmanned Navigation, Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop, Gaithersburg, MD, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822569
(Accessed December 2, 2023)