Range-Only SLAM for Underwater Navigation System with Uncertain Beacons

Chen, Wei; Sun, Rong

doi:10.1109/icmic.2018.8529843

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The RO-SLAM framework has been intensively studied [10,11]. Recently, underwater node points and unmanned boats were located using only long baseline hydroacoustic devices [12]. Menegatti et al [13] introduced RO-localization into a SLAM system consisting of an unmanned vehicle and a wireless sensor network, where the positions of sensor nodes were back-projected from multiple positions of the unmanned vehicle.…”

Section: Introductionmentioning

confidence: 99%

A 3D Range-Only SLAM Algorithm Based on Improved Derivative UKF

et al. 2022

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In this study, we constructed a 3D range-only (RO) localization algorithm based on improved unscented Kalman filtering (UKF). The algorithm can determine the location of unknown UWB nodes in a 3D environment through a moving node with low computational complexity, which can help agents to accurately identify feature points in 3D SLAM based only on the range. Specifically, we established an original UKF framework based the 3D RO localization algorithm, and developed a derivative UKF framework to reduce the computational complexity of the algorithm. We used singular value decomposition to compensate for the robustness of the algorithm. Next, we performed a theoretical analysis to show that our method reduces the computational burden without reducing the stability or accuracy of the system. Finally, we conducted numerical simulations and physical experiments to show the effectiveness of the developed 3D RO localization algorithm.

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Section: Introductionmentioning

confidence: 99%

A 3D Range-Only SLAM Algorithm Based on Improved Derivative UKF

et al. 2022

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“…An example is Simultaneous localization and mapping (SLAM), a positioning and navigation technology that uses multi-sensory information (camera, light, and LiDAR). Numerous studies in the field of underwater robots have recently focused on SLAM [ 16 , 17 , 18 ], which might be the most potential approach to achieve completely autonomous underwater robot positioning and navigation. Combining different sensory data derived from disparate underwater sensing modalities is a robust way to accomplish different underwater application scenarios (for an overview see [ 19 ]), but due to the inhomogeneity of the water column, localization remains a major challenge for sub-sea operations [ 20 ].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Bian

Hevesi

Christensen

et al. 2021

Sensors

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Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.

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