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水下SLAM技术:挑战、进展与未来方向
作者:
作者单位:

1.中国船舶及海洋工程设计研究院，上海 200011 ;2.黑龙江职业学院，黑龙江 哈尔滨 150080

作者简介:

王力锋（1993-），男，硕士，工程师，主要从事水面水下无人装备控制系统研究。

中图分类号:

TP249


Underwater SLAM Technology:Challenges,Advances,and Future Directions
Author:
Affiliation:

1.Marine Design and Research Institute of China,Shanghai 200011 ,China ;2.Heilongjiang Polytechnic,Harbin 150080 ,China

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    摘要:

    水下同步定位与地图构建（SLAM）技术是水下机器人探索未知区域的重要手段，在复杂海洋环境中广泛应用。水下 SLAM 主要分为 3 种主流技术：前视声呐 SLAM、水下光学 SLAM 及基于测深信息的 SLAM。首先，对这些技术在海底地形测绘和深海资源勘探中的应用进行了总结，不同的技术显示出不同的适用性和准确性，其中多波束声呐技术因其高分辨率和大范围覆盖能力，在大规模海底地图构建中展现了优越性。然后，总结了人工智能技术等关键技术在处理复杂海底数据中的应用，例如通过神经网络优化的数据处理提高了地图构建的效率和精度。最后，对未来的 SLAM 技术进行了展望，未来通过算法优化和硬件发展， 进一步提升数据处理速度和降低能耗。

    Abstract:

    Underwater Simultaneous Localization and Mapping（SLAM）is a crucial means for underwater robots to explore unknown areas，and is widely applied in complex marine environments. Underwater SLAM is primarily divided into three mainstream technologies：forward-looking sonar SLAM，underwater optical SLAM， and bathymetric SLAM. Firstly，a summary of these technologies in the application of seabed topography surveying and deep-sea resource exploration is provided. Different technologies show different applicability and accuracies， among which multibeam sonar technology demonstrats superiority in large-scale seabed mapping due to its high resolution and extensive coverage capabilities. Additionally，the application of key technologies such as artificial intelligence in processing complex underwater data is summarized，for instance，enhanced map construction efficiency and precision through neural network-optimized data processing. Lastly，the future of SLAM technology is envisioned. In the future，algorithm optimization and hardware development will further improve data processing speed and reduce energy consumption.

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王力锋,丛大纲,封志文.水下SLAM技术:挑战、进展与未来方向[J].数字海洋与水下攻防,2024,7(4):356-364

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  • 收稿日期:2024-04-29
  • 在线发布日期: 2024-09-07
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