Abstract
The underwater environment has some structures that still need regular inspection. However, the nature of this environment presents a number of challenges in achieving accurate vehicle position and consequently successful image similarity detection. Although there are some factors - water turbidity or light attenuation - that degrade the quality of the captured images, visual sensors have shown a strong impact on mission scenarios - close range operations. Therefore, the purpose of this paper is to study whether these data are capable of addressing the aforementioned underwater challenges on their own. Considering the lack of available data in this context, a typical underwater scenario was recreated using the Stonefish simulator. Experiments were conducted on two predefined trajectories containing appearance scene changes. The loop closure situations provided by the bag-of-words (BoW) approach are correctly detected, but it is sensitive to some severe conditions.
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Acknowledgements
This work is financed by FCT - Fundação para a Ciência e a Tecnologia - and by FSE - Fundo Social Europeu through of the Norte 2020 – Programa Operacional Regional do Norte - through of the doctoral scholarship SFRH/BD/146460/2019. This work is also financed by K2D Project - Knowledge and Data from the Deep to the Space (POCI-01-0247-FEDER-045941) funded within the scope of MIT Portugal.
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Gaspar, A.R., Nunes, A., Matos, A. (2023). Limit Characterization for Visual Place Recognition in Underwater Scenes. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 589. Springer, Cham. https://doi.org/10.1007/978-3-031-21065-5_6
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