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Quantifying Uncertainty Towards Information-Centric Unmanned Navigation

Published

Author(s)

Rajmohan (. Madhavan, Elena R. Messina

Abstract

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 Title
Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop
Conference Dates
August 16-18, 2003
Conference Location
Gaithersburg, MD, USA
Conference Title
Performance Metrics for Intelligent Systems (PerMIS) Workshop

Keywords

Bayes Theorem, Entropy, Information Evaluation, LADAR., Sensor Uncertainty, Unmanned Navigation

Citation

Madhavan, R. and Messina, E. (2003), 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 June 21, 2024)

Issues

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Created August 17, 2003, Updated October 12, 2021