Above and Below: Heterogeneous Multi-robot SLAM Across Surface and Underwater Domains

2026-05-10

AI Summary

This work addresses the challenge of achieving reliable multi-robot simultaneous localization and mapping (SLAM) for unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs) in complex aquatic environments, where reliance on acoustic ranging often proves inadequate. The paper presents the first centralized, heterogeneous multi-robot SLAM system leveraging cross-domain visual loop closure detection. By matching visual features from the shared environment observed above and below the water surface, the method enables robust USV–AUV loop closures without requiring acoustic ranging, precise clock synchronization, or unobstructed communication. All platform state estimates are fused within a unified graph optimization framework to produce a consistent map. Real-world experiments demonstrate that the proposed approach significantly reduces AUV localization error, confirming its effectiveness and robustness.

Abstract

Multi-robot simultaneous localization and mapping (SLAM) is a fundamental task in multi-robot operations. Robots must have a common understanding of their location and that of their team members to complete coordinated actions. However, multi-robot SLAM between Uncrewed Surface Vessels (USVs) and Autonomous Underwater Vehicles (AUVs) has primarily been achieved through acoustic pinging between robots to retrieve range measurements; a measurement technique requires that robots to be in similar locations simultaneously, have an uninterrupted path for signal propagation, and may necessitate synchronized clocks. This is especially challenging in complex, cluttered maritime environments, where structures may impede signals. However, these same structures may be observable above and below the water's surface, presenting an opportunity for inter-robot SLAM loop closure between USV and AUV data streams. This work builds upon recent research on inter-robot SLAM loop closure between USV and AUV data, extending it to propose a centralized multi-robot SLAM system. Each robot performs its state estimation, and we detect loop closures between each AUV and the USV data. These inter-robot loop closures are used to merge each robot's state estimate into a centralized graph, yielding estimates for the whole time history of the USV and all AUVs in the system. Validation is performed using real-world perceptual data in three different environments. Results show improved errors for AUVs in the multi-robot SLAM system compared to single-robot SLAM over the same trajectories. To our knowledge, this is the first instance of a multi-robot SLAM system with AUVs and USVs built on loop closures rather than acoustic distance measurements.

Problem

• multi-robot SLAM
• USV
• AUV
• loop closure
• underwater localization

Innovation

• multi-robot SLAM
• loop closure
• USV-AUV collaboration
• heterogeneous robotics
• centralized graph optimization