Vision-based estimation of three-dimensional position and pose of multiple underwater vehicles

Authors:
Sachit Butail;Derek A. Paley
Affiliations:
Department of Aerospace Engineering, University of Maryland, College Park, MD;Department of Aerospace Engineering, University of Maryland, College Park, MD
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

This paper describes a model-based probabilistic framework for tracking a fleet of laboratory-scale underwater vehicles using multiple fixed cameras. We model the target motion as a steered particle whose dynamics evolve on the special Euclidean group. We provide a likelihood function that extracts three-dimensional position and pose measurements from monocular images using projective geometry. The tracking algorithm uses particle filtering with selective resampling based on a threshold and nearest neighbor data association for multiple targets.We describe results obtained from two tracking experiments: first with one vehicle and a second experiment with two targets. The tracking algorithm for single target experiment is validated using data denial.