Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

Li, Chunying; Guo, Shuxiang

doi:10.1016/j.inffus.2023.02.024

Cited by 19 publications

(3 citation statements)

References 41 publications

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“…The real-time framework overcomes traditional weaknesses, demonstrating exceptional accuracy and reliability in benchmark datasets and real-world scenarios. However, potential sensor dependencies and generalization across diverse underwater settings pose considerations for further exploration [45]. Chunying Li et al introduce an innovative Multi-Source Information Fusion (MSIF) model for Spherical Underwater Robots (SURs), enhancing precision and addressing critical issues in Autonomous Underwater Vehicles (AUVs).…”

Section: Multiple Sensor Integration In Slams Odometry: Strengths And...mentioning

confidence: 99%

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Merveille,

Jia,

2024

Preprint

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Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

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Section: Multiple Sensor Integration In Slams Odometry: Strengths And...mentioning

confidence: 99%

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Merveille,

Jia,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Set a fixed sampling frequency f , collect the output value of the inertial device, collect a total of N sampling points, and obtain the sample space. Divide each n data point into M independent arrays, which can be expressed by Equation (8).…”

Section: Imu State Estimation Modelmentioning

confidence: 99%

“…Mourikis et al [7] proposed the VI-SLAM framework MSCKF in 2007. This method integrates IMU and camera data within the Extended Kalman Filter (EKF) [8] framework.…”

Section: Introductionmentioning

confidence: 99%

Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm

Zhou,

Li,

et al. 2023

Sensors

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Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.

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Secure Cooperative Control for Networked Robotic Systems with Disturbances and DoS Attacks

Li,

Wang,

Luo

et al. 2023

Secure Coordination Control of Networked Robotic Systems

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Characteristic evaluation via multi-sensor information fusion strategy for spherical underwater robots

Cited by 19 publications

References 41 publications

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm

Secure Cooperative Control for Networked Robotic Systems with Disturbances and DoS Attacks

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