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Готові списки джерел за темами / Acoustic odometry

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Добірка наукової літератури з теми "Acoustic odometry"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Acoustic odometry"

1

Ferrera, Maxime, Julien Moras, Pauline Trouvé-Peloux, and Vincent Creuze. "Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments." Sensors 19, no. 3 (February 8, 2019): 687. http://dx.doi.org/10.3390/s19030687.

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In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle. In this paper, we propose a new visual odometry method designed to be robust to these visual perturbations. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art terrestrial visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles used for underwater archaeological missions, but the developed system can be used in any other applications as long as visual information is available.

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2

Ruscio, Francesco, Giovanni Peralta, Lorenzo Pollini, and Riccardo Costanzi. "Information Communication Technology (ICT) Tools for Preservation of Underwater Environment: A Vision-Based Posidonia Oceanica Monitoring." Marine Technology Society Journal 55, no. 4 (July 1, 2021): 11–23. http://dx.doi.org/10.4031/mtsj.55.4.5.

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Abstract Underwater monitoring activities are crucial for the preservation of marine ecosystems. Currently, scuba divers are involved in data collection campaigns that are repetitive, dangerous, and expensive. This article describes the application of Information Communication Technology (ICT) tools to underwater visual data for monitoring purposes. The data refer to a Posidonia Oceanica survey mission carried out by a scuba diver using a Smart Dive Scooter equipped with visual acquisition and acoustic localization systems. An acoustic-based strategy for geo-referencing of the optical dataset is reported. It exploits the synchronization between the audio track extracted from a camera and the transponder pings adopted for the acoustic positioning. The positioning measurements are employed within an extended Kalman filter to estimate the diver's path during the mission. A visual odometry algorithm is implemented within the filter to refine the navigation state estimation of the diver with respect to the acoustic positioning only. Moreover, a smoothing step based on the Rauch-Tung-Striebel smoother is applied to further improve the estimated diver's positions. Finally, the article reports the results of two different data processing for monitoring applications. The first one is an image mosaicking obtained by concatenating subsequent frames, whereas the second one refers to a qualitative distribution of the Posidonia Oceanica over the mission area accomplished through an image segmentation process. The two outcomes are plotted over a satellite image of the surveyed area, showing that the proposed process is an effective tool capable of facilitating divers in their monitoring and inspection activities.

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3

Bobkov, Valery, Alexey Kudryashov, and Alexander Inzartsev. "A Technique to Navigate Autonomous Underwater Vehicles Using a Virtual Coordinate Reference Network during Inspection of Industrial Subsea Structures." Remote Sensing 14, no. 20 (October 13, 2022): 5123. http://dx.doi.org/10.3390/rs14205123.

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Industrial subsea infrastructure inspections using autonomous underwater vehicles (AUV) require high accuracy of AUV navigation relative to the objects being examined. In addition to traditional navigation tools with inertial navigation systems and acoustic navigation equipment, technologies with video information processing are also actively developed today. The visual odometry-based techniques can provide higher navigation accuracy for local maneuvering at short distances to objects. However, in the case of long-distance AUV movements, such techniques typically accumulate errors when calculating the AUV movement trajectory. In this regard, the present article considers a navigation technique that allows for increasing the accuracy of AUV movements in the coordinate space of the object inspected by using a virtual coordinate reference network. Another aspect of the method proposed is to minimize computational costs for AUV moving along the inspection trajectory by referencing the AUV coordinates to the object pre-calculated using the object recognition algorithm. Thus, the use of a network of virtual points for referencing the AUV to subsea objects is aimed to maintain the required accuracy of AUV coordination during a long-distance movement along the inspection trajectory, while minimizing computational costs.

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4

Chemisky, Bertrand, Fabio Menna, Erica Nocerino, and Pierre Drap. "Underwater Survey for Oil and Gas Industry: A Review of Close Range Optical Methods." Remote Sensing 13, no. 14 (July 15, 2021): 2789. http://dx.doi.org/10.3390/rs13142789.

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Анотація:

In both the industrial and scientific fields, the need for very high-resolution cartographic data is constantly increasing. With the aging of offshore subsea assets, it is very important to plan and maintain the longevity of structures, equipment, and systems. Inspection, maintenance, and repair (IMR) of subsea structures are key components of an overall integrity management system that aims to reduce the risk of failure and extend the life of installations. The acquisition of very detailed data during the inspection phase is a technological challenge, especially since offshore installations are sometimes deployed in extreme conditions (e.g., depth, hydrodynamics, visibility). After a review of high resolution mapping techniques for underwater environment, this article will focus on optical sensors that can satisfy the requirements of the offshore industry by assessing their relevance and degree of maturity. These requirements concern the resolution and accuracy but also cost, ease of implementation, and qualification. With the evolution of embedded computing resources, in-vehicle optical survey solutions are becoming increasingly important in the landscape of large-scale mapping solutions and more and more off-the-shelf systems are now available. The issues raised in this review are mainly related to the qualification of the results produced by optical systems and their limitations to cover all the needs expressed by the oil and gas industry field. Interesting qualification works of these solutions are presented in this paper as well as the use of online processing tools such as visual odometry or VSLAM to guide the data acquisition and pre-qualified survey. Finally, it seems interesting to combine acoustic and optical technologies in order to extend the field of application of these methods to low visibility conditions, which remains one of the main limiting factors in the generalization of the use of optical sensors in high resolution underwater cartography applications.

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5

Rahman, Sharmin, Alberto Quattrini Li, and Ioannis Rekleitis. "SVIn2: A multi-sensor fusion-based underwater SLAM system." International Journal of Robotics Research, July 13, 2022, 027836492211102. http://dx.doi.org/10.1177/02783649221110259.

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This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

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Дисертації з теми "Acoustic odometry"

1

Franchi, Matteo. "2D Forward Looking SONAR in Navigation Aiding: Development and Testing of Strategies for Autonomous Underwater Vehicles." Doctoral thesis, 2020. http://hdl.handle.net/2158/1183685.

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Анотація:

This work collects the results of the research activity on marine robotics carried out at the Mechatronics and Dynamic Modeling Laboratory (MDM Lab) of the Department of Industrial Engineering of the University of Florence (UNIFI DIEF) during the years 2014-2017. Reliable navigation systems are fundamental for Autonomous Underwater Vehicles (AUVs) to perform complex tasks and missions. It is well known that the Global Positioning System (GPS) cannot be employed in underwater scenarios; thus, during missions below the sea’s surface the real-time position is usually obtained with expensive sensors, such as the Doppler Velocity Log (DVL), integrated within a navigation filter such as an Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), or Dead Reckoning (DR) strategies. The main goal of this work is to develop and test a framework able to integrate a Forward-Looking SONAR (FLS), by means of linear speed estimations, into an underwater navigation system. On the one hand, the proposed solution can work together with a standard navigation sensors set (comprising, for example, a DVL), and thus leading to a greater number of linear speed measurements. On the other hand, employing an FLS to aid navigation could potentially outline other advantages. Using an augmented set of devices able to provide navigation information represents an intrinsic boost in redundancy; DVL-denied scenarios, such as very close to the seafloor or other surfaces or when a substantial number of gaseous bubbles is present, could be managed. Indeed, as opposed to the DVL, the FLS possesses much more beams that are spread into a broader area, thus improving reliability. DVL failings in the presence of bubbles are well-documented in the current literature and have been experienced during several tests at sea performed by UNIFI DIEF. Conversely, the presence of bubbles, which can be noticed within FLS images as strong return echoes spots, is usually tolerable and not capable of jeopardizing FLS operations. Moreover, although bigger AUVs enable the use of more sophisticated instrumentation and can carry a heavy payload, smaller AUVs are constrained to limited payload carrying capabilities. Hence, in addition to constituting a valuable research interest, multitasking onboard sensors represent a solution that offers compactness and avoids the use of some instruments. Besides this, to better the dynamic modeling of the AUV, a light-weight online estimator for the longitudinal dynamics and a more realistic propulsion model are developed. Lastly, an Adaptive Unscented Kalman Filter (AUKF)-based navigation solution is proposed. Offline validation, through the use of navigation data obtained during sea trials undertaken in La Spezia (Italy) at the NATO STO Centre for Maritime Research and Experimentation (CMRE), is presented. Afterward the results of real autonomous underwater missions performed in La Spezia (Italy) within the activities of the SEALab, the joint research laboratory between the Naval Experimentation and Support Center (Centro di Supporto e Sperimentazione Navale) (CSSN) of the Italian Navy and the Interuniversity Center of Integrated Systems for the Marine Environment (ISME), and at Vulcano Island, Messina (Italy) are reported.

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Тези доповідей конференцій з теми "Acoustic odometry"

1

Franchi, Matteo, Alessandro Ridolfi, and Leonardo Zacchini. "A Forward-Looking Sonar-Based System for Underwater Mosaicing and Acoustic Odometry." In 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE, 2018. http://dx.doi.org/10.1109/auv.2018.8729795.

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2

Franchi, Matteo, Alessandro Ridolfi, Leonardo Zacchini, and Benedetto Allotta. "Experimental Evaluation of a Forward-Looking Sonar-Based System for Acoustic Odometry." In OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867315.

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3

Tesei, A., M. Micheli, A. Vermeij, G. Ferri, M. Mazzi, G. Grenon, L. Morlando, et al. "An acoustic-based approach for real-time deep-water navigation of an AUV." In International Ship Control Systems Symposium. IMarEST, 2018. http://dx.doi.org/10.24868/issn.2631-8741.2018.005.

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Navigation of Autonomous Underwater Vehicles (AUVs) remains a challenge due to the impossibility to use radio frequency signals and Global Positioning System (GPS). Navigation systems usually integrate different proprioceptive sensors to estimate the asset and the speed of the vehicle. In particular, the Doppler Velocity Log (DVL) is fundamental during the navigation to have an accurate estimate of the vehicle’s speed. This work addresses the enhancement of the navigation performance of an AUV through the development of a Deep Water Navigation Filter (DWNF). The DWNF is able to work in those scenarios where traditional navigation sensors show their limits: e.g., deep waters where DVL bottom lock cannot be achieved, or areas where the use of traditionally used static and dedicated beacons is incompatible with the mission requirements. The proposed approach exploits the concept of using a network of vehicles cooperatively supporting each other for their navigation. Several types of measurements coming from the different nodes (i.e. acoustic positioning system such as ship-mounted SSBL acoustic positioning system, USBL, range measurements from the different nodes) are fused in an Extended Kalman Filter framework with the odometry data. DWNF pushes forward the idea of using a network of robotic assets as a provider of navigation services allowing more flexible and robust operations of the deployed system. The approach has been tested at sea during several experiments. We report here results from DWNF running successfully in real-time on the NATO STO-Centre for Maritime Research and Experimentation (CMRE) vehicles during the Dynamic Mongoose’17 experimentation off the South coast of Iceland (June-July 2017).

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4

Liu, Li, Ge Li, and Thomas H. Li. "ATVIO: Attention Guided Visual-Inertial Odometry." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413912.

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5

Azartash, Haleh, Kyoung-Rok Lee, and Truong Q. Nguyen. "Visual odometry for RGB-D cameras for dynamic scenes." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853803.

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6

Cheng, Hui, Yongheng Hu, Chongyu Chen, and Liang Lin. "Robust Object-Aware Sample Consensus with Application to Lidar Odometry." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461696.

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7

Liu, Hong, Peng Wei, Weibo Huang, Guoliang Hua, and Fanyang Meng. "Spatio-Temporal and Geometry Constrained Network for Automobile Visual Odometry." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9052903.

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8

Beck, Brian, and Robert Baxley. "Anchor free node tracking using ranges, odometry, and multidimensional scaling." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853991.

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9

Wang, Chengze, Yuan Yuan, and Qi Wang. "Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683446.

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10

Zhang, Lanqing, Ge Li, and Thomas H. Li. "Pose Refinement: Bridging the Gap Between Unsupervised Learning and Geometric Methods for Visual Odometry." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054700.

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