UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning

TL;DR

UIVNav is an innovative underwater navigation system that enables robots to efficiently survey objects of interest without relying on localization, using imitation learning and domain-invariant policies demonstrated in simulation and real-world experiments.

Contribution

The paper introduces UIVNav, a novel end-to-end underwater navigation approach that employs imitation learning and domain-invariant policies to improve efficiency and adaptability in underwater environments.

Findings

UIVNav outperforms random walk and coverage methods in efficiency.

The navigation policy is domain-invariant, effective across different environments.

UIVNav increases oyster survey coverage by 36% over traditional methods.

Abstract

Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient accurate localization system. We introduce UIVNav, a novel end-to-end underwater navigation solution designed to drive robots over Objects of Interest (OOI) while avoiding obstacles, without relying on localization. UIVNav uses imitation learning and is inspired by the navigation strategies used by human divers who do not rely on localization. UIVNav consists of the following phases: (1) generating an intermediate representation (IR), and (2) training the navigation policy based on human-labeled IR. By training the navigation policy on IR instead of raw data, the second phase is domain-invariant -- the navigation policy does not need to be retrained if the domain or the OOI changes. We show this by deploying the same navigation policy for surveying two different OOIs, oyster and rock reefs, in two different domains, simulation, and a real pool. We compared our method with complete coverage and random walk methods which showed that our method is more efficient in gathering information for OOIs while also avoiding obstacles. The results show that UIVNav chooses to visit the areas with larger area sizes of oysters or rocks with no prior information about the environment or localization. Moreover, a robot using UIVNav compared to complete coverage method surveys on average 36% more oysters when traveling the same distances. We also demonstrate the feasibility of real-time deployment of UIVNavin pool experiments with BlueROV underwater robot for surveying a bed of oyster shells.