Localization and Active Exploration in Indoor Underwater Environments

Abstract

Autonomous underwater vehicles have the potential to inspect and map indoor underwater environments, such as spent nuclear fuel pools and ship ballast tanks. These environments are to be regularly monitored for structural integrity—existing manual methods are expensive, dangerous and slow. Employing an autonomous agent presents distinct challenges in SLAM and exploration. This thesis makes contributions in the domains of visual localization and active SLAM for these environments.

First, we propose a novel through-water method for visual localization using landmarks above the water surface. With dead-reckoning, the vehicle pose estimate drifts and the errors propagate to the resultant map. Adopting methods from multimedia photogrammetry in our localization framework, we model refraction at the water-air interface. To the best of our knowledge, this is the first through-water method for underwater localization. We evaluate our method via both simulation and real-world experiments in a test-tank environment.

The second work presents an active SLAM framework for sonar mapping of these environments. Accurate mapping requires jointly considering the robot trajectory with the state estimation problem. Building on previous work in mapping and planning, we devise an exploration policy that bounds pose uncertainty through revisit actions. A revisit policy is selected based on submap saliency, propagated pose uncertainty, and path information gain. We demonstrate the system in simulation and highlight the advantages over an uncertainty-agnostic framework.