Exploring the DolphinSLAM's parameters

Zaffari, Guilherme B.; Santos, Matheus Machado dos; Duarte, Amanda; Fernandes, Daniel de Almeida; Botelho, Silvia Silva da Costa

doi:10.1109/oceansap.2016.7485531

Cited by 4 publications

(2 citation statements)

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“…[86] presents an algorithm for Graph SLAM based on stereo vision, and EKF (Extended Kalman Filter) has been developed and tested on the SPARUS II vehicle. Silveira et al developed a SLAM algorithm inspired by how dolphins navigate, called DolphinSLAM, using neural networks [87], [88]. The sea current was included in the training [89], and then the final DolphinSlam was compared to the EKF [90].…”

Section: A Uwsimmentioning

confidence: 99%

Underwater Simulators Analysis for Digital Twinning

Ciuccoli,

Screpanti,

Scaradozzi

2024

IEEE Access

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The underwater environment is among Earth's most challenging domains, where failures incur elevated risks and costs for both technology and human endeavors. As a consequence, forecasting the behavior of mechatronic systems through simulation has become increasingly important. Underwater Robotic Simulators (URSs) allow researchers and engineers to safely develop and assess submarine systems. The selection of an appropriate URS from the list of available tools is not trivial. Moreover, the integration of this software with the Digital Twin (DT) concept presents numerous advantages, particularly the ability to link the simulated environment with actual underwater vehicles. This connection is facilitated by performing validation and simulation tests using Software In the Loop (SIL), Model In the Loop, and Hardware In the Loop (HIL) techniques. This paper extensively reviews URSs in the context of both robots and unmanned vehicles in light of the DT paradigm. The article critically examines distinctions among existing URSs, offering valuable insights to aid researchers in selecting the most fitting tool for their specific applications. Additionally, the review explores the practical applications of the identified simulators, categorizing their usage across different fields to illuminate the preferences within the scientific community and showcase prominent case studies.

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Section: A Uwsimmentioning

confidence: 99%

Underwater Simulators Analysis for Digital Twinning

Ciuccoli,

Screpanti,

Scaradozzi

2024

IEEE Access

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“…Also, this method uses landmarks for spatial guidance and it is not dependent of a special grid system like the Cartesian method [100]. In addition, the core of the RatSLAM, had been used in the development of other neural based navigational models, like the OpenRatSLAM, that has a modular structure which is easy to implement in robots [102], and the DolphinSLAM where the properties of RatSLAM, are used in underwater environments [103].…”

Section: Hippocampus Modelsmentioning

confidence: 99%

Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

Pedro¹,

Monteiro²,

Silva³

2023

Hippocampus - More Than Just Memory

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The research of the brain has led to many questions, with most of them still not having a definitive answer. One of those questions is about how the brain acts when we navigate a new space. Inside the Temporal Lobe’s Hippocampal structure, specific types of neurons and neuronal structures are responsible to identify spatial elements. To recognize spaces, these cells require data, which is obtained from the subject’s senses. It is important to understand how these features are captured, processed, encoded and how the Hippocampus, and its neighboring elements, use the information to help in the navigation and mapping of a place. A specific type of neurons seems to support an animals location and spatial mapping, on other areas of research, discrete global grid systems are used to increase the independence of the autonomous vehicles, allowing the indexing of assets across the globe by partitioning the earth into grids that take into account the heterogeneity of the scales of the associated geospatial data. In this context, the main objective of this chapter is to make an analysis about the biological and technical aspects of navigation by establishing a bridge between the Hippocampus and Simultaneous Localization and Mapping (SLAM) methods.

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