基于 SO-CFAR 和 ADT 特征提取的 AUV SLAM 方法。

IF 2.6 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Science Progress Pub Date : 2024-10-01 DOI:10.1177/00368504241286969

Xiaokai Mu, Haiyang Chen, Jiahao Wang, Hongde Qin, Zhongben Zhu

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引用次数: 0

摘要

由于前视声纳具有卓越的探测能力，因此可用于自动潜航器（AUV）的同步定位和绘图（SLAM）。本文主要研究了基于特征图的因子图优化 SLAM 算法在 AUV 中的应用。它通过结合最小恒定误报率（SO-CFAR）和自适应阈值（ADT）来过滤前视声纳的噪声并提取特征点云，从而实现了这一目标。此外，还采用了加权迭代最邻近点（WICP）算法进行特征点配准，该算法是从声纳图像中提取的。基于现场数据的实验结果表明，与死位推算法（DR）相比，拟议方法的均方根误差（RMSE）提高了 8.52%。

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AUV SLAM method based on SO-CFAR and ADT feature extraction.

Due to the exceptional detection capabilities, the forward-looking sonar could be adopted in simultaneous localization and mapping (SLAM) for autonomous underwater vehicle (AUVs). This paper primarily investigates the application of the factor graph optimization SLAM algorithm based on feature maps in AUV. It achieves this by combining the smallest of constant false alarm rate (SO-CFAR) and adaptive threshold (ADT) to filter noise from the forward-looking sonar and extract feature point clouds. Furthermore, a weighted iterative closest point (WICP) algorithm is employed for feature point registration, which is extracted from the sonar image. The experimental result based on field data demonstrates that the proposed method, with an 8.52% improvement in root mean square error (RMSE) compared with dead reckoning (DR).

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来源期刊

Science Progress Multidisciplinary-Multidisciplinary

CiteScore

3.80

自引率

0.00%

发文量

119

期刊介绍： Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.