A Bayesian approach for tracking undersea narrow telecommunication cables

Abstract

The surveillance and inspection of underwater installations such as telecommunication cables are currently carried out by trained operators who, from the surface, guide a Remotely Operated Vehicle (ROV) with cameras mounted over it. This manual visual control is, however, a very tedious job that tends to fail if the operator looses concentration. This paper describes a tracking system for underwater narrow telecommunication cables whose main objective is to allow an Autonomous Underwater Vehicle (AUV) to video-document the whole length of a cable. The approach is based on Particle Filters (PF) because of their natural ability to model multi-dimensional multi-modal probability density functions, what allows handling in a more appropriate way the ambiguities which naturally arise from undersea environments. The paper also describes a set of features added to the standard structure of a PF, which successfully compensate some large errors in the cable pose estimation when the non-enhanced tracker is applied. Extensive experimental results over a test set of more than 10,000 frames, for which a ground truth has been manually generated, have shown the usefulness of the solution proposed. Besides, results for a set of six video sequences accounting for almost 150,000 frames and around one hour and a half of successful continuous video tracking are also discussed. All those images come from inspection runs captured by ROVs navigating over real telecommunication undersea cables.