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水下SLAM与深度学习相遇:挑战,多传感器集成和未来方向

Mohamed Heshmat¹, Lyes Saad Saoud¹, Muayad Abujabal¹

¹Khalifa University Center for Autonomous and Robotic Systems, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.

Sensors (Basel, Switzerland)

|September 19, 2025

概括

深度学习通过在具有挑战性的水下条件下改进同时定位和映射 (SLAM) 来增强自主水下车辆导航. 这项调查分析了深度学习技术,并提出了一个新的框架,将水下无线传感器网络集成为更强大的AUV操作.

科学领域:

机器人技术机器人技术机器人技术
海洋技术海洋技术
人工智能的人工智能

背景情况:

自主水下车辆 (AUV) 需要强大的同时定位和映射 (SLAM) 来在复杂的水下环境中进行导航.
传统的SLAM方法与可见度差,动态照明,传感器噪音和水失真等问题作斗争,限制了AUV的准确性和可靠性.
深度学习 (DL) 提供先进的解决方案来克服这些水下SLAM挑战.

研究的目的:

为水下应用提供深度学习增强的SLAM技术的全面调查.
批判性地评估当前基于DL的水下SLAM方法的好处和局限性.
为水下SLAM引入一个新的分类框架,集成水下无线传感器网络 (UWSNs).

主要方法:

基于方法,传感器依赖和DL集成的DL增强的水下SLAM方法的分类.
对现有技术进行批判性评估,识别创新和挑战.
为水下SLAM开发一个新的分类学,将UWSN纳入协作传感和通信.

主要成果:

DL显著改善了水下SLAM的特征提取,无声化,扭曲校正和传感器融合.
拟议的UWSN集成框架增强了AUV本地化,映射和实时数据共享.
新兴趋势包括变压器架构,多模式融合,轻量级网络和自我监督学习.

结论:

用DL增强的SLAM对于强大的水下航行至关重要,解决固有的环境挑战.
将UWSN与SLAM集成为提高AUV运营效率和准确性提供了一个有希望的方向.
需要进一步研究先进的DL架构和多模式融合,以推进自主水下勘探.

关键词:

基于DL的SLAM基于DL的SLAM 深度学习 (DL) 是指深度学习.同时定位和绘制 (SLAM)在水下 SLAM 鱼.水下图像增强水下图像增强水下机器人水下机器人

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