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

Xiaomin Lin, Nare Karapetyan, Kaustubh Joshi, Tianchen Liu, Nikhil Chopra, Miao Yu, Pratap Tokekar, Yiannis Aloimonos

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 UIVN AV , 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. UIVN AV utilizes imitation learning and draws inspiration from the navigation strategies employed by human divers, who do not rely on localization. UIVN AV 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 UIVN AV 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 UIVN AV 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 UIVN AV in pool experiments with BlueROV underwater robot for surveying a bed of oyster shells.