解决动态水下环境长期监测中的图像定位与对齐难题
Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments
- 融合视觉位置识别、特征匹配与图像分割，实现水下场景精准重定位
- 在跨越数日到数年的长时间跨度下仍保持高定位准确率
- 适用于水下机器人长期生态监测，尤其适合复杂光照与浑浊环境
水下生态系统有效监测对于追踪环境变化、指导保护行动和保障长期生态健康至关重要。然而，由于水下影像的复杂性，传统视觉定位方法难以应用，自动化水下生态管理仍面临挑战。本文提出一个整合视觉位置识别（VPR）、特征匹配与图像分割的管道，基于视频生成的图像实现对重访区域的鲁棒识别、刚性变换估计及后续生态变化分析。此外，我们引入SQUIDLE+ VPR基准——首个大规模水下VPR基准，利用多平台机器人采集的非结构化数据，时间跨度从天到年不等，涵盖多样轨迹、任意重叠度和不同海床类型，覆盖深度、光照与浊度等多变环境条件。代码已开源：https://github.com/bev-gorry/underloc
原文摘要 · Abstract (English)
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
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