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Exploring 3-D Reconstruction Techniques: A Benchmarking Tool for Underwater Robotics.

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Title: Exploring 3-D Reconstruction Techniques: A Benchmarking Tool for Underwater Robotics.
Authors: Perez, Javier1, Sales, Jorge2, Penalver, Antonio1, Fornas, David1, Fernandez, Jose Javier1, Garcia, Juan Carlos1, Sanz, Pedro J.1, Marin, Raul1, Prats, Mario1
Source: IEEE Robotics & Automation Magazine. Sep2015, Vol. 22 Issue 3, p85-95. 11p.
Subjects: Robotics engineers, Robotic exoskeletons, Motion detectors, Robotics, Robot motion, Robot control systems, Electronic control, Automation
Abstract: When the results of research in the field of robotics are presented to the scientific community, the same question is asked repeatedly: Are the results really reproducible? Regarding benchmarking issues, some technological areas where complex mechatronic devices, such as robots, have a central role are very far from other research areas like physics or chemistry, to name but a few, where reproducibility is always mandatory. Aside from mechatronic complexities, the comparison between two different algorithms in the same conditions is influenced by the experimental validation scenario. In underwater environments, the difficulties for benchmarking characterization increase substantially. This is especially true when the test bed is the sea where uncertainty is high. It is the aim of this article to present a software tool which enables a comparison between two different algorithms when the algorithms are being used to solve the same problem in water tank conditions. This is a preliminary stage before the final validation on the seabed. The evaluated algorithms fall into the three-dimensional (3-D) image reconstruction context, as a prior step to their autonomous manipulation. Performance results are presented for both simulation and real water tank conditions. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Robotics & Automation Magazine is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: When the results of research in the field of robotics are presented to the scientific community, the same question is asked repeatedly: Are the results really reproducible? Regarding benchmarking issues, some technological areas where complex mechatronic devices, such as robots, have a central role are very far from other research areas like physics or chemistry, to name but a few, where reproducibility is always mandatory. Aside from mechatronic complexities, the comparison between two different algorithms in the same conditions is influenced by the experimental validation scenario. In underwater environments, the difficulties for benchmarking characterization increase substantially. This is especially true when the test bed is the sea where uncertainty is high. It is the aim of this article to present a software tool which enables a comparison between two different algorithms when the algorithms are being used to solve the same problem in water tank conditions. This is a preliminary stage before the final validation on the seabed. The evaluated algorithms fall into the three-dimensional (3-D) image reconstruction context, as a prior step to their autonomous manipulation. Performance results are presented for both simulation and real water tank conditions. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Robotics & Automation Magazine is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/MRA.2015.2448971
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              Text: Sep2015
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