Modular Multi-Sensor Fusion for Underwater Localization for Autonomous ROV Operations

Abstract

Localization filters for underwater vehicles are mostly tailored for specific sensor suites, environments, or missions. It is also well known that the underwater environment can evolve over time and throughout the mission, affecting the vehicle’s sensors, e.g., tide, currents, and vehicle proximity to structures, especially in harbor areas. In this paper, the Modular and Robust Sensor-Fusion Framework (MaRS) is extended to work with underwater vehicles and their environment. It enables efficient use of asynchronous sensors and handles measurement outliers and outages. Sensor-frame initialization and online extrinsic calibration methods are also explored. Tests are performed in real harbor-like environments using a small remotely operated vehicle (ROV) and show improved handling of sensors and state estimation results.