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

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.