Quantifying Uncertainty Towards Information-Centric Unmanned Navigation

Published

August 18, 2003

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

Pub Type

Conferences

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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 April 23, 2024)