Robust underwater SLAM fusing bathymetric and range information

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2024-11-13 DOI:10.1016/j.measurement.2024.116223

Zhu Yixian , Ma Teng , Fan Jiajia , Jiang Yanqing , Li Ye , Liao Yulei , Qi Chi

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引用次数: 0

Abstract

Bathymetric simultaneous localization and mapping (BSLAM) can accurately locate an autonomous underwater vehicle (AUV) in deep-sea missions, but it cannot provide real-time location results for vehicles prior to revisit due to the lack of overlap between bathymetric measurements. A real-time estimate of the vehicle’s location can not only help the vehicle to follow the given path accurately but reduce the number of invalid bathymetric associations caused by the nearly flat seafloor terrain. In this paper, we proposed a bathymetric and range information fused underwater SLAM (BRSLAM) method that estimates the vehicle’s position in real time by combining both bathymetric data and range measurements. In particular, a probabilistic graph partitioning algorithm (PGPA) was proposed to identify range outliers, and an information-theoretic node reduction method was presented to reduce the computational complexity of BRSLAM. Playback experiments showed that the BRSLAM can provide accurate navigation results with no more than 4 beacons.

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融合测深和测距信息的鲁棒水下 SLAM

水深同步定位和绘图（BSLAM）可以在深海任务中准确定位自主潜水器（AUV），但由于水深测量之间缺乏重叠，它无法在重访之前为潜水器提供实时定位结果。对潜航器位置的实时估计不仅能帮助潜航器准确跟踪给定路径，还能减少因近乎平坦的海底地形而导致的无效测深关联。在本文中，我们提出了一种水深和测距信息融合的水下 SLAM（BRSLAM）方法，该方法通过结合水深数据和测距数据来实时估计车辆的位置。其中，提出了一种概率图分割算法（PGPA）来识别测距离群值，并提出了一种信息论节点缩减方法来降低 BRSLAM 的计算复杂度。回放实验表明，BRSLAM 可以在不超过 4 个信标的情况下提供精确的导航结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.