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.

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Sachit Butail, Derek A. Paley
• Keywords: IROS 2009, Laboratory-scale Underwater Vehicles, Model-based Probabilistic Framework, Robotics, Tracking Algorithm