深度学习增强的微型机器人集群在危险水下环境中的视觉监测
Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
AI总结：
本文提出一种集成数据仿真、多模态深度学习坐标预测与图像重组的新方法，解决危险水下环境中微型机器人集群因环境干扰导致的位置漂移和旋转问题，实现高精度对齐与清晰连贯的环境监测。
摘要：
对水下存储设施等极端环境进行长期监测和探索，成本高昂、劳动密集且具有危险性。使用低成本、协作型机器人自动化这一过程可以大幅提高效率。这些机器人从不同位置捕获图像，必须同时处理这些图像以创建设施的时空模型。在本文中，我们提出了一种新颖的方法，该方法集成了数据仿真、用于坐标预测的多模态深度学习网络和图像重组，以应对由环境干扰导致机器人位置和方向漂移与旋转所带来的挑战。我们的方法通过整合来自快照的视觉信息、来自掩码的全局位置上下文以及带噪声的坐标，提高了噪声环境中的对齐精度。我们通过使用模拟水下环境中真实世界机器人操作的合成数据进行了大量实验，验证了我们的方法。结果表明，坐标预测精度极高且图像拼接合理，证明了我们方法的现实适用性。组装后的图像为有效监测和检查提供了清晰连贯的水下环境视图，展示了在极端环境中更广泛应用的潜力，进一步有助于提高危险现场监测的安全性、效率和成本降低。代码可在 https://github.com/ChrisChen1023/Micro-Robot-Swarm 获取。
英文摘要：
Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must be processed simultaneously to create a spatio-temporal model of the facility. In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations. Our approach enhances the precision of alignment in noisy environments by integrating visual information from snapshots, global positional context from masks, and noisy coordinates. We validate our method through extensive experiments using synthetic data that simulate real-world robotic operations in underwater settings. The results demonstrate very high coordinate prediction accuracy and plausible image assembly, indicating the real-world applicability of our approach. The assembled images provide clear and coherent views of the underwater environment for effective monitoring and inspection, showcasing the potential for broader use in extreme settings, further contributing to improved safety, efficiency, and cost reduction in hazardous field monitoring. Code is available on https://github.com/ChrisChen1023/Micro-Robot-Swarm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
