Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments

2025-03-06

AI Summary

Visual place recognition (VPR) fails in long-term underwater ecological monitoring due to severe illumination variations, high turbidity, and texture scarcity. Method: This paper proposes a VPR-matching-segmentation co-design framework: (1) we introduce SQUIDLE+ VPR—the first large-scale, cross-temporal, multi-platform, multi-environment underwater VPR benchmark; (2) we integrate SuperPoint/SuperGlue-based feature matching, semantic segmentation, and video frame sequence alignment to achieve robust scene re-identification, rigid registration, and interpretable ecological change analysis. Contribution/Results: Experiments demonstrate substantial improvements in relocalization accuracy and generalization across long-term (day-to-year), low-texture, and highly dynamic underwater environments. The framework has been validated on real autonomous underwater vehicles, confirming its efficacy for quantitative ecological change assessment.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional visual localization methods. We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images. This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes. Furthermore, we introduce the SQUIDLE+ VPR Benchmark-the first large-scale underwater VPR benchmark designed to leverage an extensive collection of unstructured data from multiple robotic platforms, spanning time intervals from days to years. The dataset encompasses diverse trajectories, arbitrary overlap and diverse seafloor types captured under varying environmental conditions, including differences in depth, lighting, and turbidity. Our code is available at: https://github.com/bev-gorry/underloc

Problem

• Automating underwater ecosystem monitoring with robotic platforms
• Challenges in underwater imagery for traditional visual localization
• Developing a method for robust identification and analysis of ecosystem changes

Innovation

• Combines VPR, feature matching, image segmentation
• Introduces SQUIDLE+ VPR Benchmark
• Enables robust relocalization in underwater environments