UIVNAV: Underwater Information-Driven Vision-Based Navigation Via Imitation Learning

Autonomous navigation in the underwater en- vironment 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 navigate robots over Objects of Interest (OOI) while avoiding obstacles, all without relying on localization. UIVNAV utilizes imitation learning and draws inspiration from the navigation strategies employed 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 demonstrate this within simulation by deploying the same navigation policy to survey two distinct Objects of Interest (OOIs): oyster and rock reefs. We compared our method with complete coverage and random walk methods, showing that our approach is more efficient in gathering information for OOIs while 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 UIVNAV in pool experiments with BlueROV underwater robot for surveying a bed of oyster shells.