面向机器人视觉海底测绘的半分层重建与弱区重访
Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping
AI总结：
本文提出一种导航辅助的半分层重建方法，结合SLAM与全局SfM，通过识别并重访弱重建区域，实现高效、完整且一致的大范围海底三维建图。
摘要：
尽管近几十年来许多陆上视觉建图算法取得了令人瞩目的成果，但由于恶劣的环境条件，将这些方法从陆地转移到深海仍然是一个挑战。自主水下航行器（AUV）配备高分辨率相机和人工照明系统，其捕获的图像除了光线折射外，还常常受到衰减和散射引起的非均匀光照和质量退化的影响。这些挑战往往导致陆上SLAM方法在水下应用时失败，或导致SfM方法出现漂移或遗漏具有挑战性的图像。因此，这会导致地图出现空洞、跳变或弱重建区域。在这项工作中，我们提出了一种导航辅助的分层重建方法，以促进数公顷海底的自动化机器人三维重建。我们的分层方法结合了SLAM和全局SfM的优势，比增量式SfM效率高得多，同时确保全局地图的完整性和一致性。这是通过识别并重访有问题或弱重建的区域来实现的，避免遗漏图像并更好地利用有限的潜水时间。所提出的系统已在多次研究航行中进行了广泛测试和评估，证明了其在真实世界条件下的鲁棒性和实用性。
英文摘要：
Despite impressive results achieved by many on-land visual mapping algorithms in the recent decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Images captured by autonomous underwater vehicles (AUVs), equipped with high-resolution cameras and artificial illumination systems, often suffer from heterogeneous illumination and quality degradation caused by attenuation and scattering, on top of refraction of light rays. These challenges often result in the failure of on-land SLAM approaches when applied underwater or cause SfM approaches to exhibit drifting or omit challenging images. Consequently, this leads to gaps, jumps, or weakly reconstructed areas. In this work, we present a navigation-aided hierarchical reconstruction approach to facilitate the automated robotic 3D reconstruction of hectares of seafloor. Our hierarchical approach combines the advantages of SLAM and global SfM that is much more efficient than incremental SfM, while ensuring the completeness and consistency of the global map. This is achieved through identifying and revisiting problematic or weakly reconstructed areas, avoiding to omit images and making better use of limited dive time. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real-world conditions.
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