Toward long-term, automated ship hull inspection with visual SLAM, explicit surface optimization, and generic graph-sparsification

Summary

Paul Ozog and Ryan M. Eustice, Toward long-term, automated ship hull inspection with visual SLAM, explicit surface optimization, and generic graph-sparsification. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 3832-3839, Hong Kong, China, June 2014.

Abstract

This paper reports on a method for an autonomous underwater vehicle to perform real-time visual simultaneous localization and mapping (SLAM) on large ship hulls over multiple sessions. Along with a monocular camera, our method uses a piecewise-planar model to explicitly optimize the ship hull surface in our factor-graph framework, and anchor nodes to co-register multiple surveys. To enable real-time performance for long-term SLAM, we use the recent Generic Linear Constraints (GLC) framework to sparsify our factor-graph. This paper analyzes how our single-session SLAM techniques can be used in the GLC framework, and describes a particle filter reacquisition algorithm so that an underwater session can be automatically re-localized to a previously built SLAM graph. We provide real-world experimental results involving automated ship hull inspection, and show that our localization filter outperforms Fast Appearance-Based Mapping (FAB-MAP), a popular place-recognition system. Using our approach, we can automatically align surveys that were taken days, months, and even years apart.

Bibtex entry