scholarly journals Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition

2021 ◽  
Author(s):  
Adam Safron ◽  
Ozan Çatal ◽  
Tim Verbelen
Keyword(s):  
Free Energy ◽  
Latent Variable ◽  
Dynamic Equilibrium ◽  
Fundamental Problem ◽  
Simultaneous Localization And Mapping ◽  
Ecological Rationality ◽  
Balance Model ◽  
Levels Of Abstraction ◽  
Localization And Mapping ◽  
High Level

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which hippocampal/entorhinal system (H/E-S) adaptations have been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S ‘design’ properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop closure, graph relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally achieved in FEP-AI agents, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting the impacts of these neuromodulators on H/E-S functioning may potentially illuminate the adaptive significance of these signaling pathways as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals A survey of image semantics-based visual simultaneous localization and mapping: Application-oriented solutions to autonomous navigation of mobile robots

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091918
Author(s):  
Linlin Xia ◽  
Jiashuo Cui ◽  
Ran Shen ◽  
Xun Xu ◽  
Yiping Gao ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Open Problems ◽  
Typical Application ◽  
Image Semantics ◽  
Localization And Mapping ◽  
Mapping System ◽  
High Level ◽  
Pipeline Design ◽  
Environmental Understanding

As one of the typical application-oriented solutions to robot autonomous navigation, visual simultaneous localization and mapping is essentially restricted to simplex environmental understanding based on geometric features of images. By contrast, the semantic simultaneous localization and mapping that is characterized by high-level environmental perception has apparently opened the door to apply image semantics to efficiently estimate poses, detect loop closures, build 3D maps, and so on. This article presents a detailed review of recent advances in semantic simultaneous localization and mapping, which mainly covers the treatments in terms of perception, robustness, and accuracy. Specifically, the concept of “semantic extractor” and the framework of “modern visual simultaneous localization and mapping” are initially presented. As the challenges associated with perception, robustness, and accuracy are being stated, we further discuss some open problems from a macroscopic view and attempt to find answers. We argue that multiscaled map representation, object simultaneous localization and mapping system, and deep neural network-based simultaneous localization and mapping pipeline design could be effective solutions to image semantics-fused visual simultaneous localization and mapping.

scholarly journals Postpartum depression symptoms in survey-based research: a structural equation analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Che Wan Jasimah Bt Wan Mohamed Radzi ◽  
Hashem Salarzadeh Jenatabadi ◽  
Nadia Samsudin
Keyword(s):  
Postpartum Depression ◽  
Latent Variable ◽  
Structural Equation ◽  
Depression Scale ◽  
Public Health Problem ◽  
Equation Modeling ◽  
Depression Symptoms ◽  
Postpartum Women ◽  
Unhealthy Food ◽  
High Level

Abstract Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

scholarly journals Research on Laser-Visual Fusion-based Simultaneous Localization and Mapping

2020 ◽  
Vol 1682 ◽  
pp. 012049
Author(s):  
Jianjie Zhenga ◽  
Haitao Zhang ◽  
Kai Tang ◽  
Weidi Kong
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Localization And Mapping

scholarly journals Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 48-61
Author(s):  
Bhavyansh Mishra ◽  
Robert Griffin ◽  
Hakki Erhan Sevil
Keyword(s):  
System Integration ◽  
Ad Hoc ◽  
Simultaneous Localization And Mapping ◽  
Software Systems ◽  
Model Driven ◽  
Performance Improvements ◽  
Software Model ◽  
Localization And Mapping ◽  
Stereo Cameras ◽  
Core Components

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

scholarly journals Model-Driven Architectural Framework towards Safe and Secure Nuclear Power Reactors

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5136
Author(s):  
Bassem Ouni ◽  
Christophe Aussagues ◽  
Saadia Dhouib ◽  
Chokri Mraidha
Keyword(s):  
Nuclear Power ◽  
Nuclear Reactors ◽  
System Development ◽  
Levels Of Abstraction ◽  
Model Driven ◽  
Case System ◽  
Architectural Framework ◽  
Performance Check ◽  
High Level ◽  
Different Levels

Sensor-based digital systems for Instrumentation and Control (I&C) of nuclear reactors are quite complex in terms of architecture and functionalities. A high-level framework is highly required to pre-evaluate the system’s performance, check the consistency between different levels of abstraction and address the concerns of various stakeholders. In this work, we integrate the development process of I&C systems and the involvement of stakeholders within a model-driven methodology. The proposed approach introduces a new architectural framework that defines various concepts, allowing system implementations and encompassing different development phases, all actors, and system concerns. In addition, we define a new I&C Modeling Language (ICML) and a set of methodological rules needed to build different architectural framework views. To illustrate this methodology, we extend the specific use of an open-source system engineering tool, named Eclipse Papyrus, to carry out many automation and verification steps at different levels of abstraction. The architectural framework modeling capabilities will be validated using a realistic use case system for the protection of nuclear reactors. The proposed framework is able to reduce the overall system development cost by improving links between different specification tasks and providing a high abstraction level of system components.

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