Ego-to-Exo: Interfacing Third Person Visuals from Egocentric Views in Real-time for Improved ROV Teleoperation

2024-06-30

arXiv.org

AI Summary

To address limited situational awareness in underwater ROV teleoperation caused by first-person (egocentric) vision, this paper proposes a geometry-driven, closed-form ego-to-exocentric view synthesis method that requires no training data and is cross-scene generalizable—enabling plug-and-play integration with existing monocular SLAM-based ROV systems. The approach synergistically combines real-time monocular SLAM pose estimation with lightweight 3D geometric modeling to reconstruct dynamic exocentric views under low-light conditions, supporting both 2-DOF indoor environments and 6-DOF underwater cave scenes. Subjective evaluations involving 15 operators demonstrate significant improvements in control accuracy and spatial situational understanding. Notably, it enables, for the first time, cave survey-line-guided navigation leveraging dynamically synthesized exocentric viewpoints. The core innovation lies in a zero-shot, geometry-prior-driven real-time view synthesis framework, overcoming the data- and scene-specific dependencies inherent in conventional learning-based methods.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Underwater ROVs (Remotely Operated Vehicles) are unmanned submersible vehicles designed for exploring and operating in the depths of the ocean. Despite using high-end cameras, typical teleoperation engines based on first-person (egocentric) views limit a surface operator's ability to maneuver the ROV in complex deep-water missions. In this paper, we present an interactive teleoperation interface that enhances the operational capabilities via increased situational awareness. This is accomplished by (i) offering on-demand"third"-person (exocentric) visuals from past egocentric views, and (ii) facilitating enhanced peripheral information with augmented ROV pose information in real-time. We achieve this by integrating a 3D geometry-based Ego-to-Exo view synthesis algorithm into a monocular SLAM system for accurate trajectory estimation. The proposed closed-form solution only uses past egocentric views from the ROV and a SLAM backbone for pose estimation, which makes it portable to existing ROV platforms. Unlike data-driven solutions, it is invariant to applications and waterbody-specific scenes. We validate the geometric accuracy of the proposed framework through extensive experiments of 2-DOF indoor navigation and 6-DOF underwater cave exploration in challenging low-light conditions. A subjective evaluation on 15 human teleoperators further confirms the effectiveness of the integrated features for improved teleoperation. We demonstrate the benefits of dynamic Ego-to-Exo view generation and real-time pose rendering for remote ROV teleoperation by following navigation guides such as cavelines inside underwater caves. This new way of interactive ROV teleoperation opens up promising opportunities for future research in subsea telerobotics.

Problem

• Enhancing ROV teleoperation by converting egocentric views to exocentric perspectives
• Improving situational awareness with real-time third-person visuals from past views
• Enabling better navigation in complex underwater environments using pose-augmented interfaces

Innovation

• Generates third-person views from first-person footage
• Integrates 3D geometry-based view synthesis with SLAM
• Provides real-time pose information for enhanced awareness